This is a preliminary study and the objective of this study has been to reconstruct of missing parts or scratches of digital images is an important field used extensively in artwork restoration. This restoration can be done by using two approaches, image inpainting, and texture synthesis. There are many techniques for the two previous approaches that can carry out the process optimally and accurately. In this paper, the advantages and disadvantages of most algorithms of the image inpainting approach are discussed. Among the different algorithms, the proposed dynamic masking method outperformed than other techniques. This modification produces rapid and simple for reconstruction of small missing and damaged portions of images that are two to three orders of magnitude faster than current methods while producing comparable results with respect to other.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
The document discusses key concepts in digital image fundamentals including:
1. The electromagnetic spectrum and how light attributes like intensity and luminance are measured.
2. How digital images are acquired through image sensing and sampling/quantization.
3. Methods for representing digital images through matrices and binary values, and how resolution affects gray-level detail.
4. Digital zooming techniques like nearest neighbor, bilinear, and bicubic interpolation that control blurring and edge effects.
5. Concepts like pixel adjacency, connectivity, and distance measures between pixels.
The document discusses key concepts regarding digitized images and their properties. It covers topics like image functions, image digitization through sampling and quantization, metric properties of digital images including distance and adjacency, topological properties, histograms, and types of noise in images like additive noise and salt and pepper noise. The document provides detailed explanations of these concepts along with illustrative examples.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
The document discusses key concepts in image processing including image sensing, acquisition, formation, sampling, quantization, and digital representation. It describes how the human eye forms images and contains photoreceptor cells. There are three main types of image sensors: single, line, and array. Sampling converts a continuous image to digital by selecting pixel values at regular intervals while quantization assigns discrete brightness levels. Together they allow images to be represented digitally as matrices of pixel values.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
The document discusses key concepts in digital image fundamentals including:
1. The electromagnetic spectrum and how light attributes like intensity and luminance are measured.
2. How digital images are acquired through image sensing and sampling/quantization.
3. Methods for representing digital images through matrices and binary values, and how resolution affects gray-level detail.
4. Digital zooming techniques like nearest neighbor, bilinear, and bicubic interpolation that control blurring and edge effects.
5. Concepts like pixel adjacency, connectivity, and distance measures between pixels.
The document discusses key concepts regarding digitized images and their properties. It covers topics like image functions, image digitization through sampling and quantization, metric properties of digital images including distance and adjacency, topological properties, histograms, and types of noise in images like additive noise and salt and pepper noise. The document provides detailed explanations of these concepts along with illustrative examples.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
The document discusses key concepts in image processing including image sensing, acquisition, formation, sampling, quantization, and digital representation. It describes how the human eye forms images and contains photoreceptor cells. There are three main types of image sensors: single, line, and array. Sampling converts a continuous image to digital by selecting pixel values at regular intervals while quantization assigns discrete brightness levels. Together they allow images to be represented digitally as matrices of pixel values.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)Ulaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fuzzy Connectivity (FC) – Affinity functions
• Absolute FC
• Relative FC (and Iterative Relative FC)
• Successful example applications of FC in medical imaging
• Segmentation of Airway and Airway Walls using RFC based method
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
Image pre processing - local processingAshish Kumar
The document discusses various image pre-processing techniques, including:
1) Local pre-processing methods like smoothing and gradient operators that use a neighborhood of pixels to calculate output pixel values.
2) Common smoothing methods include averaging, median filtering, and techniques that average only similar neighboring pixels to reduce blurring.
3) Gradient operators like Roberts, Prewitt, Sobel, and Kirsch detect edges by approximating the image derivative using pixel differences. The Marr-Hildreth technique detects zero-crossings of the second derivative.
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This document provides an overview of digital image processing. It discusses what digital image processing is, provides a brief history, and outlines some of the key stages involved, including image acquisition, enhancement, restoration, morphological processing, segmentation, representation and description, object recognition, and compression. It also discusses some example applications like medical imaging, autonomous vehicles, traffic monitoring, and biometrics. The document uses images to illustrate different concepts and stages in digital image processing.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
This document discusses image enhancement techniques in the spatial domain. It defines spatial domain processing as the direct manipulation of pixel values, as opposed to frequency domain processing which modifies the Fourier transform. The key techniques discussed are:
- Linear and non-linear transformations which map input pixel values to new output values.
- Spatial filters which operate on neighborhoods of pixels, including smoothing filters to reduce noise and sharpening filters to enhance edges.
- Histogram processing techniques like equalization to improve contrast in low contrast images.
The document provides examples of each technique and discusses their applications in image enhancement.
This document provides an overview of digital image processing and is divided into multiple parts. Part I discusses digital image fundamentals, image transforms, image enhancement, image restoration, image compression, and image segmentation. It introduces key concepts such as digital image systems, sampling and quantization, pixel relationships, and image transforms in both the spatial and frequency domains. Image processing techniques like filtering, histogram processing, and frequency domain filtering are also summarized.
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
3.point operation and histogram based image enhancementmukesh bhardwaj
The document discusses various techniques for digital image enhancement, including point operations, histogram equalization, and frequency domain methods. Point operations directly map input pixel values to output values using functions like contrast stretching and clipping. Histogram equalization maps values to equalize the image histogram for better contrast. Frequency methods like unsharp masking and homomorphic filtering enhance images in the frequency domain by modifying high and low frequency components. The techniques can be used to improve images for applications in digital photography, iris recognition, microscopy, and entertainment.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
The document describes techniques for image texture analysis and segmentation. It proposes a methodology using constraint satisfaction neural networks to integrate region-based and edge-based texture segmentation. The methodology initializes a CSNN using fuzzy c-means clustering, then iteratively updates the neuron probabilities and edge maps to refine the segmentation. Experimental results demonstrate improved segmentation by combining region and edge information.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
This document provides an overview of digital image processing. It discusses key topics including digital image fundamentals, image transforms, image enhancement, image restoration, image compression, image segmentation, representation and description, and recognition and interpretation. The document outlines concepts and techniques within each of these topics at a high level over multiple sections and pages with headings, content lists, and explanatory diagrams.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
The document discusses digital image representation and processing. It covers:
1) How digital images are represented as 2D arrays of integer pixel values stored in computer memory.
2) The main types of digital images - binary, grayscale, and true color images - based on the number of possible values per pixel.
3) Common image processing techniques like segmentation, thresholding, and histograms that analyze and modify digital images.
4) Thresholding converts pixels to black/white based on a threshold and is often used in segmentation. Histograms show pixel value distributions to aid analysis.
An Enhanced Model for Inpainting on Digital Images Using Dynamic MaskingMd. Shohel Rana
Given an image with significant portions missing or damaged. Dynamically detect the damaged regions to be inpainted. Reconstitute missing regions with data consistent with the rest of the image. Proposed a method which restore damaged area of the image reducing processing time without blurring output.
The document describes a new image scaling algorithm based on an area pixel model that aims to achieve low complexity suitable for VLSI implementation. It presents an edge-oriented area-pixel scaling processor that uses an approximate technique to calculate pixel areas with 6-bit integers rather than floating point values. It also employs a simple edge catching technique to better preserve edges. The proposed 7-stage VLSI architecture was implemented using Verilog HDL and synthesized using a 0.18-micron process, achieving a processing rate of 200MHz with 10.4K gate counts. Experimental results showed it performs better than other lower complexity methods in terms of quality and speed.
Lec9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)Ulaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fuzzy Connectivity (FC) – Affinity functions
• Absolute FC
• Relative FC (and Iterative Relative FC)
• Successful example applications of FC in medical imaging
• Segmentation of Airway and Airway Walls using RFC based method
Digital image processing img smoothningVinay Gupta
The document discusses image smoothing and sharpening techniques in digital image processing. It begins by defining what a digital image is and the goals of digital image processing. Then it discusses various applications of digital image processing like image enhancement, medical visualization, and human-computer interfaces. Key techniques covered include image smoothing using spatial filters to average pixel values in a neighborhood and image sharpening using spatial filters based on spatial differentiation to highlight edges. Examples of the Hubble space telescope and facial recognition are also mentioned.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
Image pre processing - local processingAshish Kumar
The document discusses various image pre-processing techniques, including:
1) Local pre-processing methods like smoothing and gradient operators that use a neighborhood of pixels to calculate output pixel values.
2) Common smoothing methods include averaging, median filtering, and techniques that average only similar neighboring pixels to reduce blurring.
3) Gradient operators like Roberts, Prewitt, Sobel, and Kirsch detect edges by approximating the image derivative using pixel differences. The Marr-Hildreth technique detects zero-crossings of the second derivative.
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This document provides an overview of digital image processing. It discusses what digital image processing is, provides a brief history, and outlines some of the key stages involved, including image acquisition, enhancement, restoration, morphological processing, segmentation, representation and description, object recognition, and compression. It also discusses some example applications like medical imaging, autonomous vehicles, traffic monitoring, and biometrics. The document uses images to illustrate different concepts and stages in digital image processing.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
This document discusses image enhancement techniques in the spatial domain. It defines spatial domain processing as the direct manipulation of pixel values, as opposed to frequency domain processing which modifies the Fourier transform. The key techniques discussed are:
- Linear and non-linear transformations which map input pixel values to new output values.
- Spatial filters which operate on neighborhoods of pixels, including smoothing filters to reduce noise and sharpening filters to enhance edges.
- Histogram processing techniques like equalization to improve contrast in low contrast images.
The document provides examples of each technique and discusses their applications in image enhancement.
This document provides an overview of digital image processing and is divided into multiple parts. Part I discusses digital image fundamentals, image transforms, image enhancement, image restoration, image compression, and image segmentation. It introduces key concepts such as digital image systems, sampling and quantization, pixel relationships, and image transforms in both the spatial and frequency domains. Image processing techniques like filtering, histogram processing, and frequency domain filtering are also summarized.
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
3.point operation and histogram based image enhancementmukesh bhardwaj
The document discusses various techniques for digital image enhancement, including point operations, histogram equalization, and frequency domain methods. Point operations directly map input pixel values to output values using functions like contrast stretching and clipping. Histogram equalization maps values to equalize the image histogram for better contrast. Frequency methods like unsharp masking and homomorphic filtering enhance images in the frequency domain by modifying high and low frequency components. The techniques can be used to improve images for applications in digital photography, iris recognition, microscopy, and entertainment.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
The document describes techniques for image texture analysis and segmentation. It proposes a methodology using constraint satisfaction neural networks to integrate region-based and edge-based texture segmentation. The methodology initializes a CSNN using fuzzy c-means clustering, then iteratively updates the neuron probabilities and edge maps to refine the segmentation. Experimental results demonstrate improved segmentation by combining region and edge information.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
This document provides an overview of digital image processing. It discusses key topics including digital image fundamentals, image transforms, image enhancement, image restoration, image compression, image segmentation, representation and description, and recognition and interpretation. The document outlines concepts and techniques within each of these topics at a high level over multiple sections and pages with headings, content lists, and explanatory diagrams.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
The document discusses digital image representation and processing. It covers:
1) How digital images are represented as 2D arrays of integer pixel values stored in computer memory.
2) The main types of digital images - binary, grayscale, and true color images - based on the number of possible values per pixel.
3) Common image processing techniques like segmentation, thresholding, and histograms that analyze and modify digital images.
4) Thresholding converts pixels to black/white based on a threshold and is often used in segmentation. Histograms show pixel value distributions to aid analysis.
An Enhanced Model for Inpainting on Digital Images Using Dynamic MaskingMd. Shohel Rana
Given an image with significant portions missing or damaged. Dynamically detect the damaged regions to be inpainted. Reconstitute missing regions with data consistent with the rest of the image. Proposed a method which restore damaged area of the image reducing processing time without blurring output.
The document describes a new image scaling algorithm based on an area pixel model that aims to achieve low complexity suitable for VLSI implementation. It presents an edge-oriented area-pixel scaling processor that uses an approximate technique to calculate pixel areas with 6-bit integers rather than floating point values. It also employs a simple edge catching technique to better preserve edges. The proposed 7-stage VLSI architecture was implemented using Verilog HDL and synthesized using a 0.18-micron process, achieving a processing rate of 200MHz with 10.4K gate counts. Experimental results showed it performs better than other lower complexity methods in terms of quality and speed.
Vector-Based Back Propagation Algorithm of.pdfNesrine Wagaa
This document presents a vector-based backpropagation algorithm for a supervised convolution neural network (CNN) model. The key points are:
- The CNN model consists of one convolution layer followed by three fully connected hidden layers for classification of handwritten digits using the MNIST dataset.
- The classical convolution operation is replaced by a matrix operation to avoid mathematical complexities. Convolution maps and filters are represented as vectors.
- Forward propagation involves applying the new convolution and pooling operations to extract features, then passing the output through the fully connected layers.
- Backpropagation is used to update the CNN parameters (filters, weights, biases) via gradient descent to minimize a cost function, with update equations derived for both the convolution
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document presents a comparison of two image inpainting techniques - curvature driven diffusion (CDD) inpainting and total variation (TV) inpainting. The paper aims to apply these two inpainting methods to grayscale and color images to restore damaged regions. CDD inpainting works by solving partial differential equations of isophote intensity, while TV inpainting is based on texture filling. Experimental results on various images are shown to demonstrate the effectiveness of the two approaches. The document also discusses related work, provides implementation details of the two methods, and outlines potential future work including hardware implementation.
This document discusses atmospheric turbulence degraded image restoration using back propagation neural network. It proposes using a feed-forward neural network with 20 hidden layers and one output layer trained with backpropagation to restore images degraded by atmospheric turbulence and noise. The network is trained on normalized input images and tested on blurred images. Results show the proposed method achieves higher PSNR values than other techniques like kurtosis minimization and PCA, indicating better image quality restoration. Future work may incorporate median filtering and using first order image features for network weight assignment.
This document proposes a method for change detection in images that combines Change Vector Analysis, K-Means clustering, Otsu thresholding, and mathematical morphology. It involves detecting intensity changes using CVA, segmenting the difference image using K-Means, calculating a threshold with Otsu's method, applying the threshold and morphological operations, and comparing results to other change detection techniques. Experimental results on medical and other images show the proposed method achieves satisfactory change detection with fewer errors compared to other methods.
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
This document discusses an efficient approach for image segmentation and blind deconvolution in image restoration. It begins with introducing concepts of image restoration, segmentation, and blind deconvolution. It then presents the proposed methodology which uses Gaussian blurring, segments the blurred image into 9 parts, and applies blind deconvolution to restore each segment. The quality of restored segments is measured using PSNR and MSE. Experimental results on various images show the proposed technique provides better restoration than existing methods, as measured by higher PSNR and lower MSE. In conclusion, blind deconvolution with segmentation effectively restores selected image regions while reducing computational complexity compared to other techniques.
AU QP Answer key NOv/Dec 2015 Computer Graphics 5 sem CSEThiyagarajan G
This document contains a summary of a computer graphics exam with 10 multiple choice questions in Part A and 4 long answer questions in Part B. Some of the key topics covered include: image resolution, scaling matrices, color conversion between RGB and CMY color modes, Bezier curves, projection planes, dithering, animation principles, turtle attributes in graphics, Bresenham's circle algorithm, Liang-Barsky line clipping algorithm, viewing transformations, cubic Bezier curves, and backface detection. Part B also includes questions on orthographic vs axonometric vs oblique projections, ambient lighting models, raster vs keyframe animation, ray tracing, and morphing.
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Review of Linear Image Degradation and Image Restoration TechniqueBRNSSPublicationHubI
This document summarizes a review paper on linear image degradation and restoration techniques. It discusses two main topics:
1. Image degradation sources including blurring from optical systems, motion, and noise. Blurring can be space-invariant or space-variant. Noise can be correlated or uncorrelated.
2. Image restoration techniques including non-blind and blind methods. Non-blind methods know the blurring function beforehand while blind methods estimate the blurring function. Restoration can also be constrained or unconstrained.
1. Computed tomography (CT) image reconstruction involves estimating digital images from measured x-ray projection data. Early methods included back projection, which was simple but produced blurred images.
2. Modern commercial CT scanners use analytical methods like filtered back projection or Fourier filtering to reduce blurring. These methods apply spatial or frequency domain filters to projection data before back projecting to reconstruct the image.
3. Iterative reconstruction methods were also developed and provide better image quality than analytical methods but are too computationally intensive for clinical use. Current research aims to make iterative methods fast enough for real-time medical imaging.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
This document provides an introduction to fundamentals of image processing. It defines key concepts such as digital images, image sampling, and common image processing tools. Digital images are represented as arrays of pixels with integer brightness values. Common image processing tools introduced include convolution, Fourier transforms, and different types of image operations and neighborhoods that can be used. The document also discusses video standards and parameters for digitized video images.
Deblurring Image and Removing Noise from Medical Images for Cancerous Disease...IRJET Journal
This document summarizes a research paper that uses a Wiener filter to deblur and remove noise from medical images for cancer detection. The paper introduces different types of image blurring and noise, as well as deblurring and noise removal techniques. It then describes experiments using a Wiener filter on blurred and noisy medical images. The Wiener filter is shown to effectively deblur images and remove noise, improving image quality as measured by metrics like PSNR, MSE, RMSE and SSIM. The findings suggest the Wiener filter is a powerful tool for processing medical images.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
The corpus callosum (CC) is the largest white-matter structure in human brain. In this paper, we take two techniques to observe the results of segmentation of Corpus Callosum. The first one is mean shift algorithm and morphological operation. The second one is k-means clustering. In this paper, it is performed in three steps. The first step is finding the corpus callosum area using adaptive mean shift algorithm or k-means clustering . In second step, the boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) mode and final step to remove unknown noise using morphological operation and evolved to get the final segmentation result. The experimental results demonstrate that the mean shift algorithm and k-means clustering has provided a reliable segmentation performance.
This document summarizes a seminar presentation on an image denoising method based on the curvelet transform. The presentation covered:
1) How image noise occurs and traditional denoising methods like linear filters and edge-preserving smoothing.
2) The curvelet transform process including sub-band decomposition, smooth partitioning, renormalization, and ridgelet analysis.
3) An image denoising algorithm that applies wavelet and curvelet transforms, then combines results using quad tree decomposition.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
This document is a seminar report on digital image processing submitted by a student, N.Ch. Karthik, in partial fulfillment of a Bachelor of Technology degree. It discusses correcting raw images by subtracting dark current and bias, flat fielding for pixel sensitivity variations, and displaying images by limiting histograms, using transfer functions, and histogram equalization. The report also covers mathematical image manipulations and references other works.
This is a preliminary study and the objective of this study is to make simple distributed database system with some basic tutorials. Cassandra is a distributed database from Apache that is highly scalable and designed to accomplish very large amounts of organized data. Without having a single point of failure, it offers high accessibility. This report highlights with a basic outline of Cassandra trailed by its architecture, installation, and significant classes and interfaces. Subsequently, it proceeds to cover how to perform operations such as CREATE, ALTER, UPDATE, and DELETE on KEYSPACES, TABLES, and INDEXES using CQLSH using C#/.NET Client with a sample program done by ASP.NET(C#).
A Proposed PST Model for Enhancing E-Learning ExperiencesMd. Shohel Rana
Md. Shohel Rana presented a proposed PST (Parents-Student-Teacher) model for enhancing e-learning experiences at the 2017 International Conference on Education and Distance Learning in Maldives. The model aims to allow teachers, students, and parents to collaborate on a single online platform from remote locations. It incorporates features like online lesson sharing, communication tools, assessment tools, and a smart board for interactive lessons. Diagrams show how the data and collaboration would flow within the proposed model. The presentation concluded by stating that the model could improve classroom efficiency and communication between all parties involved in education.
Speckle Noise Reduction in Ultrasound Images using Adaptive and Anisotropic D...Md. Shohel Rana
US Imaging Technique less cost. Nonlinear and Anisotropic filter for removing speckle noise can be removed from US images. Proposed a modified Anisotropic filter which reduces speckle noises.
Comparing the Performance of Different Ultrasonic Image Enhancement Technique...Md. Shohel Rana
Medical ultrasound US images are usually corrupted by speckle noise during their acquisition. De-noising techniques are to remove noises while retaining the important signal features. Preservation of the image sharpness and details while suppressing the speckle noise. A novel restoration scheme has been introduced for ultrasound (US) images for speckle reduction which enhances the signal-to-noise ratio while conserving the edges and lines in the image
Malware analysis on android using supervised machine learning techniquesMd. Shohel Rana
In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android.
Developing visual material can help to recall memory and also be a quick way to show lots of information. Visualization helps us remember (like when we try to picture where we’ve parked our car, and what's in our cupboards when writing a shopping list). We can create diagrams and visual aids depicting module materials and put them up around the house so that we are constantly reminded of our learning
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
1. Date : December 6, 2017
Location : USM
VISUAL TECHNIQUES, COS-701
1
Presented by:
Md. Shohel Rana
Instructed By:
Dr. Parthapratim Biswas
DE-CONVOLUTION ON DIGITAL IMAGES
2. CONTENTS
• Motivation and Contribution
• What is Inpainting? Why?
• Convolution? Why?
• Convolution based inpainting
• Various Inpainting Techniques on Image
• Image Measurement Matrices
• Proposed Method
• Experiment and Result
• Discussion on Result
• Conclusion and Future Works
VISUAL TECHNIQUES, COS-701
2
3. MOTIVATION AND CONTRIBUTION
• Given image with significant portions missing or damaged
• Dynamically detect the damaged regions to be inpainted
• Reconstitute missing regions with data consistent with the rest of the
image
• Proposed a method which restore damaged area of the image
reducing processing time without blurring output
VISUAL TECHNIQUES, COS-701
3
4. INPAINTING? WHY?
• Reconstruction of missing or damaged portions of images is an ancient
practice used broadly in artwork restoration
• The activity consists of filling in the missing areas or modifying the damaged
ones in a non-detectable way by an observer not familiar with the original
images
• Applications of image inpainting range from restoration of photographs, films
and paintings, to removal of occlusions, such as text, subtitles, stamps and
publicity from images.
• Inpainting is an artistic synonym for image interpolation, and has been
circulated among museum restoration artists for a long time
• As an ancient painting gets older, the pigments in certain parts start falling
off the canvas, and the painting becomes incomplete
• The human act of filling in the missing parts of a painting is called
"inpainting“ as first introduced to image processing by Bertalmio, Sapiro,
Caselles, and Ballester at the University of Minnesota (SIGGRAPH 2000)
[1]-[2] VISUAL TECHNIQUES, COS-701
4
5. INPAINTING? WHY? (CONT.)
• Inpainting technique can be categorized as follows:
Convolution Filter based method
Partial Differential Equation (PDE)
Others Algorithm
• In general all convolution based methods provide good results
• The convolution based equation is as follows
Where Iout is inpainted image, I is the input image W is the Mask and M x N is size of the Image
VISUAL TECHNIQUES, COS-701
5
∑∑= =
=
M
i
N
j
out jiWjiIjiI
0 0
),(),(),(
6. CONVOLUTION? WHY?
• Convolution is a mathematical way of combining two signals to form a third signal. It
is the single most important technique in Digital Signal Processing.
• A system changes an input signal into an output signal.
• First, the input signal can be decomposed into a set of impulses, each of which can be viewed
as a scaled and shifted delta function.
• Second, the output resulting from each impulse is a scaled and shifted version of the impulse
response.
• Third, the overall output signal can be found by adding these scaled and shifted impulse
responses. In other words, if we know a system's impulse response, then we can calculate
what the output will be for any possible input signal
• The impulse response goes by a different name in some applications. If the system
being considered is a filter, the impulse response is called the filter kernel, the
convolution kernel. In image processing, the impulse response is called the point
spread function.
• Convolution helps to understand a system’s behavior based on current and past
events
VISUAL TECHNIQUES, COS-701
6
7. CONVOLUTION? WHY? (CONT.)
• The below figure shows a simple convolution problem: a 9 point input
signal x[n] is passed through a system with a 4 point impulse response,
h[n], resulting in a 9+4-1=12 point output signal, y[n]. In mathematical
terms, x[n] is convolved with h[n] to produce y[n]. This first viewpoint of
convolution is based on the fundamental concept of DSP: decompose
the input, pass the components through the system, and synthesize the
output.
VISUAL TECHNIQUES, COS-701
7
9. VARIOUS INPAINTING TECHNIQUES ON
IMAGE
Let us assume that image I is of size M × N. Let (i, j) be the pixel location
inside the inpainting region Ω, and Ω is the area to be inpainted where δΩ its
boundary.
•Bertalmio’s algorithm: The equation for image I with inpainting region Ω
Where Ixx(i,j) and Iyy(i,j) are second-order derivatives of image at pixel (i, j): 1 ≤ i ≤ M,1≤ j ≤ N along axes x
and y, respectively. The change in L along the direction is given by
•Let, In (i, j) denote each of the image pixels inside the region Ω at the
inpainting “time” n. Then, with an improvement factor the inpainting equation is
given by
VISUAL TECHNIQUES, COS-701
9
( ) ( )
Ω∈∀∆∆+=+
),(|,),(|),(),(1
jijiItjiIjiI nnnn
β
|),,(|
),,(
),(:),(
njiN
njiN
jiLji nn
⋅= δβ
),(),(),( jiIjiIjiL n
yy
n
xx
n
+=
10. VARIOUS INPAINTING TECHNIQUES ON
IMAGE
• Oliveira Algorithm: Proposed an inpainting algorithm deleting color
information inside the mask, followed by edge detection for the
occluded/damaged area. Starting from the pixels on the edge, a convolution
operation is then applied, using a neighborhood centered on each contour
pixel and one of the proposed kernels [3]-[5]
• Oliviera method takes the image to inpaint on selected region by convolving
with averaging filter has a zero weight at the center
Where the values of a, b, and c for both kernels are 0.073235, 0.176765, and 0.125 respectively
VISUAL TECHNIQUES, COS-701
10
∑∑
∑∑
= =
= =
=
=
M
i
N
j
out
M
i
N
j
jiWjiIjiI
jiWjiIjiI
0 0
/
0 0
/
),(2).,(),(
),(1).,(),(
11. VARIOUS INPAINTING TECHNIQUES ON
IMAGE (CONT.)
• Hadhoud, Moustafa, and Shenoda’s Algorithm: They have proposed an
improvement of Oliveira’s method with reducing processing time with convoluting
the region with averaging filter has a zero weight at bottom right corner instead of
center. [4]-[6]
• The method uses a differently defined convolution kernel by using more known
neighbors, and the restoration process can be achieved even within a single
iteration
Where the values of a, b, and c for both kernels are 0.073235, 0.176765, and 0.125
respectively
VISUAL TECHNIQUES, COS-701
11
∑∑
∑∑
= =
= =
=
=
M
i
N
j
out
M
i
N
j
jiWjiIjiI
jiWjiIjiI
0 0
/
0 0
/
),(2).,(),(
),(1).,(),(
12. VARIOUS INPAINTING TECHNIQUES ON
IMAGE (CONT.)
• H. Noori, S. Saryazdi, H. Nezamabadi: Uses an adaptive kernel permitting a better
processing edge regions. To do this, it uses the gradient of known pixels in the
neighborhood of a missed pixel to compute weights in convolving mask W(x) by
proposing a function F(x) to compute weights from the image gradient. [7]-[8]
• Selecting a missed pixel on boundary of the damaged region, next considering a
neighborhood around it and central gradients for each recognized pixel in the mask W(x),
is then calculated. Then finally, a value for a damaged pixel is calculated as
Where k presents the pixel position, x is gradient value of the current pixel in the image, α is a parameter
giving an estimation of the missed pixel gradient control the softness of propagation and f'(p) is estimated
value, f(k) is value of a known pixel, n is the number of known pixel in the current neighborhood.
VISUAL TECHNIQUES, COS-701
12
≥
≤≤−
≤−
=
α
α
α
α
α
α
||0
||
2
)1(
2
||)(1
)( 2
2
xif
xif
x
xif
x
xF
)(
1
)( kxF
n
xw =
)())()())(1()( 11
/
kfkwpfkwpf
n
k
n
k ∑∑ −−
+−=
13. IMAGE MEASUREMENT MATRICES
• RMSE: The root mean square error
• PSNR: Peak Signal to Noise Ratio is computed by
VISUAL TECHNIQUES, COS-701
13
),,.
1
(
1 1
2
)(∑∑ −
= =
=
M
i
N
j
yx jijiMN
RMSE
)max(.20
2
10
log RMSEPSNR g=
14. PROPOSED METHOD
In case of proposed method
•Inpaint damaged portion of the image without blurring output with taking less
time
•Dynamically detect the inpainted region with removing image’s noise
•Use only one Kernel/Mask where Oliveira method uses two kernels and
replacing the values of kernel
VISUAL TECHNIQUES, COS-701
14
0.080000 0.170000 0.080000
0.170000 0.080000 0.170000
0.080000 0.170000 0
15. PROPOSED METHOD (CONT.)
1. Input Image with damage information
2. Color Image Converted in to Gray scale
3. Filter the image to remove noise using median filter
4. Find edge of Imidusing “Canny Edge Detector” and smoothing image to
reduce the number of connected components producing Icanny
VISUAL TECHNIQUES, COS-701
15
)(
)(
]:1[]:1[
II
WI
W
sortmid
nsort
n
mid
sort
MNIF
=
=
=
16. PROPOSED METHOD (CONT.)
5. Resize the image to produce mask image Imaskwhich gives the target
region to be inpainted from Icanny
6. Calculate connected components to extract all the connected components.
7. Create 3x3 Window/kernel W and convolve the image using mask image
and following equation by checking damage using
8. Print Output and compute execution time
VISUAL TECHNIQUES, COS-701
16
∑∑= =
=
M
i
N
j
out jiWjiIjiI
0 0
),().,(),(
18. EXPERIMENT AND RESULT (CONT.)
VISUAL TECHNIQUES, COS-701
18
Figure/
Metho
d
Oliveira
(seconds)
Noori
(seconds)
Hadhoud
(seconds)
Proposed
(seconds)
Fig-a 98.2739 86.0736 9.6012 27.0833
Fig-b 42.7693 37.4856 7.1990 15.2771
Fig-c 77.6711 63.4485 9.0671 21.4831
Figure/
Method
Oliveira Noori Hadhoud Propose
d
Fig-a 12.0129 7.021
5
12.9902 13.2219
Fig-b 12.7494 9.124
8
14.4092 15.1507
Fig-c 15.1130 6.225
1
13.1350 13.0310TABLE 1: COMPARISON STUDY OF EXECUTION TIME OF PROPOSED METHOD
WITH OTHERS
TABLE 2: COMPARISON STUDY OF PSNROF PROPOSED METHOD WITH
OTHERS
19. RESULT AND DISCUSSION
During the last couple of years, a certain number of inpainting methods
have been proposed, but it is still difficult to determine the appropriate
one and also important to determine the algorithm parameters that lead
to the best PSNR results and selecting representative images to
provide relevant information. We have proposed a simple convolution
based model which faster than others’ algorithm with creating a
dynamic kernel to detect the damaged area to be inpainted. Our
proposed method can also substitute or restore the background when
removing the large object from the image by removing noise without
blurring the image.
VISUAL TECHNIQUES, COS-701
19
20. CONCLUSION AND FUTURE WORK
• It has been demonstrated that proposed technique is capable of
restoring damaged/occluded 2D image
• It gives best result among those algorithms with reducing time without
blurring images
• Used image extension was “.jpeg”
• It can be used in 3D images
• Try to preserve edge and inpaint image with asymmetric background
VISUAL TECHNIQUES, COS-701
20
21. REFERENCES
1. M. Bertalmio, Im ag e Inpainting , Proc. of SIGGRAPH 2000, Computer Graphics Processings, 417–424.
2. C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera, IEEE Trans. Img. Proc., 10, 1200-1211, 2001.
3. M. Oliveira, B. Bowen, R. Mckenna, and Y. S. Chang, “Fast dig italim ag e inpainting ”, in Proc. VIIP, 2001, pp. 261–266.
4. R. Vreja and R. Brad, “Im ag e inpainting m e tho ds e valuatio n and im pro ve m e nt”, The Scientific World Journal, vol. 2014,
p. 11, 2014.
5. F. Hollaus, “Diffe re nt m e tho ds fo r im ag e inpainting ”, Vienna University of Technology.
6. M. M. Hadhoud, K. A. Moustafa, and S. Z. Shendoa, “Dig ital im ag e s inpainting using m o difie d co nvo lutio n base d
m e tho d”, International Journal of Signal Processing, Image Procesing and Pattern Recognition, 2005.
7. H. Noori, S. Saryazdi, and H. Nezamabadi-Pour, “A co nvo lutio n base d im ag e inpainting ”, in Proc. 1st
International
Conference on Communications Engineering, 22–24, December 2010.
8. R. Kamran, M. Nasri, H. Nezamabadi-pour, and S. Saryazdi, “Ane w ve cto r m e tho d fo r co lo r im ag e inpainting ”, in Proc.
First National Conference on New Ideas in Electrical Engineering, 2012.
9. A. Telea, “An im ag e inpainting te chniq ue base d o n the fast m arching m e tho d ”, Journal of Graphics Tools, vol. 9, no. 1,
2004.
VISUAL TECHNIQUES, COS-701
21