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.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
Removal of Unwanted Objects using Image Inpainting - a Technical ReviewIJERA Editor
Image In painting, the technique to change image in undetectable structure, it itself is an ancient art. There are
various goals and applications of image in painting which includes restoration of damaged painting and also to
replace/remove the selected objects. This paper, describes various techniques that can help in removing
unwanted objects from image. Even the in painting fundamentals are directly further, most inpainting techniques
available in the literature are difficult to understand and implement.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
In the criminal investigation field, images are the principal forms for investigation and for probing crime detection. The imaging science applied in criminal investigation is face detection, surveillance camera imaging, and crime scene analysis. Digital imaging succors image manipulation, alteration and enhancement techniques. The traditional methodologies enhance the given image by improving the local or global components of the image. It proves a debacle since it engages noise amplification, block discontinuities, colour mismatch, edge distortion and checkerboard effects thereby limiting image processing tasks. To the same degree of enhancement, spurned artefacts are given rise. Thus to balance the global and local factors of the image and to weed out the tenebrous components; fusion of multiple alike images are performed to produce a meliorated image. The fusion is done by fusing a pyramid constructed image and a wavelet transformed image. The pyramid image and the wavelet transformed image are then fused through to afford a revealing image for better perception by the human visual system. The experimental results show that our proposed fusion scheme is effective and the fusion is applied over a surveillance camera image grab.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
Evaluation of graphic effects embedded image compression IJECEIAES
A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
In the criminal investigation field, images are the principal forms for investigation and for probing crime detection. The imaging science applied in criminal investigation is face detection, surveillance camera imaging, and crime scene analysis. Digital imaging succors image manipulation, alteration and enhancement techniques. The traditional methodologies enhance the given image by improving the local or global components of the image. It proves a debacle since it engages noise amplification, block discontinuities, colour mismatch, edge distortion and checkerboard effects thereby limiting image processing tasks. To the same degree of enhancement, spurned artefacts are given rise. Thus to balance the global and local factors of the image and to weed out the tenebrous components; fusion of multiple alike images are performed to produce a meliorated image. The fusion is done by fusing a pyramid constructed image and a wavelet transformed image. The pyramid image and the wavelet transformed image are then fused through to afford a revealing image for better perception by the human visual system. The experimental results show that our proposed fusion scheme is effective and the fusion is applied over a surveillance camera image grab.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
Evaluation of graphic effects embedded image compression IJECEIAES
A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Comparative Study and Analysis of Image Inpainting TechniquesIOSR Journals
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.
Similar to An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration (20)
An Examination of Effectuation Dimension as Financing Practice of Small and M...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Does Goods and Services Tax (GST) Leads to Indian Economic Development?iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Childhood Factors that influence success in later lifeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emotional Intelligence and Work Performance Relationship: A Study on Sales Pe...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer’s Acceptance of Internet Banking in Dubaiiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study of Employee Satisfaction relating to Job Security & Working Hours amo...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumer Perspectives on Brand Preference: A Choice Based Model Approachiosrjce
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An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. I (Nov – Dec. 2015), PP 41-46
www.iosrjournals.org
DOI: 10.9790/0661-17614146 www.iosrjournals.org 41 | Page
An Efficient Approach of Segmentation and Blind Deconvolution
in Image Restoration
Sindhu Jain1
, Mr. Sudhir Goswami2
1
(Computer Science, M.I.E.T, Meerut, India)
2
(Asst. Prof. Computer Science, M.I.E.T, Meerut, India)
Abstract :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.
Keywords - Blind Deconvolution, Image Restoration, Image Segmentation, MSE ,PSF, PSNR value.
I. Introduction
An Image is a word that is derived from a Latin word ‘imago’ which is a representation of visual
perception in a 2-D or 3-D picture that has a similar appearance to some objects and a Digital Image is a
numeric representation of a 2-D image [1].
1.1. Image Restoration:
Image Restoration is a process of undo all the unnecessary effects known as ‘blur’ or ‘noise’ that has
been added into an image by various reasons like misfocus of the camera, large distance between camera an
object, capturing a picture of a moving object known as motion blur, Gaussian blur by using Gaussian filter etc.
The process of adding noise into a digital image is known as Convolution and the process of restoring a
convoluted image is known as Deconvolution. There are basically 3 types of blur: a) Motion blur, b) Gaussian
blur, c) Average blur [2]. The main objective of Image Restoration is to sharpen the features of an image in such
a way that it can clearly be seen by a person. However Image Restoration differs from Image Enhancement. In
Image Enhancement we do not make use of any degradation model or we do not need to know about the process
which is degrading the image while in case of Image Restoration, degradation model is necessary to restore an
image. In Image restoration we try to find a degradation model and then by using that model, we apply the
inverse process and try to restore the image [3].
Degradation model of Image Restoration:
Fig (1): Degradation Model
2. An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
DOI: 10.9790/0661-17614146 www.iosrjournals.org 42 | Page
Some applications of Image Restoration are:
1.1.1. In medical science such as CT, MRI, Ultrasound etc.
1.1.2. Sharpness.
1.1.3. Contrast Enhancement.
1.1.4. Denoising etc.
1.2. Image segmentation: Image segmentation divides an image into various parts or segments also known ‘set
of pixels’ or ‘Super Pixels’ in which each pixel or picture element exhibits similar attributes. There are various
techniques for image segmentation such as region based segmentation, edge/ boundary based segmentation etc
[4]. The segmentation technique used in this paper is based on Pixels known as Pixel Based Segmentation. In
this technique a digital image is divided on the basis of the resolution of an image. Resolution is the number of
pixels on the horizontal axis and the vertical axis of the image. Segmentation of image depends on the size of
the input image. Larger is the size of the Input image and more is the number of segments of that image. This
paper considers 9 segments of the input image.
1.3. Blind Deconvolution: Blind Deconvolution is a technique of restoration of a degraded/ blurred image
without having any knowledge of blur kernel or PSF(Point Spread Function) of an image. A kernel is a mask in
the form of small matrix used to blur an image. This small matrix is known as Convolution Matrix. In this
technique of restoration, blur kernel is unknown that is why it is known as ‘Blind’. Deconvolution is a technique
to sharpen or deblur a blurry image, and collectively it is known as Blind Deconvolution Technique. A kernel is
a 2D matrix that is used to blur an image also known as Convolution Matrix. It can be represented as:
Y=k*X+n; (1)
Where, X is the input gray image. Y is the degraded image. K is the kernel or convolution matrix that is added
with the input image X to transform it into the blurry image called Y. * is the convolution operator.
The goal of Blind Deconvolution is to inverse the above process and to recover both X and k [5]. This technique
restores the blurry image by calculating PSF of the degradation by using three techniques and chooses the one
that provides better restoration result. Techniques are:
1.3.1. Undersized PSF.
1.3.2. Oversized PSF.
1.3.3. INIT PSF.
The performance of restoration is measured by calculating PSNR (Peak Signal to Noise Ration) value
of the restored image. Higher is the value of the PSNR, more will be the quality of restored image.
II. Proposed Methodology
This methodology firstly convert a digital colored image into gray image (if it not a gray image) and then add
Gaussian blur into that image. Gaussian blur is added into the image by using following function:
H=fspecial (grayimagename, hsize, sigma); (2)
Where, hsize can be a vector or a scalar. This function returns a Gaussian filter of size ‘hsize’ with standard
deviation ‘sigma’. fspecial () creates Gaussian 2D filter by using following formula:
hg(n1,n2)=𝑒
−(𝑛12+𝑛 22)
2𝜎2
(3)
h(n1,n2)=
hg(n1,n2)
ℎ 𝑔𝑛2𝑛1
(4)
Where hg is a Gaussian Filter having n1 number of rows and n2 number of columns. 𝜎 is a standard deviation.
After adding Gaussian Blur into a Gray image, the technique of segmentation is applied on the image.
Number of segments depends upon the size of segments. Larger is the size of image more will be the number of
segments.
Architectural model of proposed technique is shown in figure Fig(2):
3. An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
DOI: 10.9790/0661-17614146 www.iosrjournals.org 43 | Page
Fig (2): Architectural Model Of Proposed Technique.
Algorithm of Proposed Technique:
Step 1: Read the Original Gray Image I.
Step 2: Add blur into image I say G.
Step 3: Apply pixel based segmentation on image I and corresponding image G. ( No. of segments depend upon
the size of Input image.
Where Original Gray Image I contains, I=(I1,I2,I3,I4……) segments and Blurred Image G contains,
G=(G1,G2,G3,G4…..) segments.
Step 4: Store all segments found in step 3 into a folder.
Step 5: for all G=(G1,G2,G3,G4….) apply Blind Deconvolution Algorithm.
Step 6: Remove ringing effects from the restored segments for accuracy of results.
Step 7: Measure performance of the result by calculating PSNR values.
III. Quality Measurement
Quality of an image in proposed technique is generally measured in terms of PSNR (Peak Signal-to-Noise
Ratio) and the MSE(Mean Square Error).
Peak Signal-to-Noise Ratio (PSNR) is a mathematical measure of image quality based on pixel
difference between two images [6]. It is generally measured in terms of decibels(db). Higher the PSNR value of
the image, better is the quality of that image. PSNR of two images can be calculated as:
PSNR=10log
𝑀𝐴𝑋 2
𝑀𝑆𝐸
(5)
Where, MAX is 255 for 8 bit images. MSE is Mean Square Error
Mean Square Error (MSE) is calculated by averaging the squared intensity of the original image and
the output image pixels [7]. It can be calculated as follows:
MSE=
1
𝑀𝑁
[𝑋 𝑖, 𝑗 − 𝑌 𝑖, 𝑗 ]2𝑁−1
𝑗=0
𝑀=1
𝑖=0 (6)
IV. Experimental Results
4.1.Without Segmentation: Proposed technique is applied on Four Gray Images without segmentation having
different image types. Following table shows original gray images, their corresponding degraded images and
images obtained after applying Blind Deconvolution Technique:
Image Name Original Image Degraded Image Restored Image
Lenna.png
(300*300)
BLURRED IMAGE
INPUT GRAY IMAGE
ADD GAUSSIAN BLURADD GAUSSIAN BLUR SEGMENTATION
CALCULATE PSNR
VALUES
APPLY BLIND
DECONVOLUTION ALGORITHM
REMOVE RINGING
EFFECTS
RESTORED SEGMENTS
CALCULATE
PSNR VALUES
4. An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
DOI: 10.9790/0661-17614146 www.iosrjournals.org 44 | Page
Man.jpg
(300*300)
Cameraman.png
(300*300)
Flower.jpg
(300*300)
PSNR values of images shown in Table(1) using proposed technique:
Table(2)
S.NO. Name of the Image Mean Square Error(MSE) PSNR
(With blurred image)
PSNR
(With restored image)
1 Lenna.png 18.3528 32.2211 35.4938
2 Man.jpg 25.9320 32.7216 33.9924
3 Cameraman.png 36.7227 28.6653 32.4815
4 Flower.png 34.1088 30.7325 32.8021
5 Flower.jpg 34.1115 34.1115 32.8018
Comparison of Proposed Technique with Existing Method:
Table(3)
S.NO. Name of the Image Mean Square
Error(MSE)
Peak Signal-to-Noise
Ratio(PSNR)
Existing Method
MSE PSNR
1 Cameraman.tif
(256*256)
18.3308 35.4990 126.79 27.10
2 Lenna.png
(256*256)
28.1503 33.6360 90.74 28.55
3 Cameraman.tiff
(128*128)
34.6017 32.7398 167.50 25.89
Graphical Representation Of Comparison Between Proposed Technique And Existing Technique:
Fig (3): Comparison of PSNR VALUES of Table(3)
5. An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
DOI: 10.9790/0661-17614146 www.iosrjournals.org 45 | Page
Fig (4): Comparison of MSE of Table(3)
4.2. With Segmentation: Proposed technique is applied on images ‘Cameraman.png‘ and ‘Lenna.jpg’. In this
technique an image is divided into nine segments and then each segment is restored separately such that they
have their own PSNR value. This method works well when we do not want to restore whole image. We are able
to restore only selected portion of the image.
Segments of Original Gray Image ‘Cameraman.png’:
Fig(5): Original segments Fig(6): Degraded Segments Fig(7): Restored Segments
PSNR Values of above Segments are shown in Table(3):
Table(3)
Lenna.jpg
Segment
No.
Mean Square
Error(MSE)
PSNR
(With blurred
image)
PSNR
(With restored
image)
Cameraman.png
Segment No.
Mean Square
Error(MSE)
PSNR
(With blurred
image)
PSNR
(With restored
image)
1 25.7804 32.3628 34.0179 1 49.6027 30.8297 31.1758
2 32.4765 30.6622 33.0151 2 58.0593 31.6346 30.4921
3 50.2705 28.9009 31.1177 3 56.1695 29.9950 30.6358
4 23.2537 34.1951 34.4659 4 103.0218 27.0308 28.0015
5 46.2948 29.8002 31.4755 5 154.0187 24.7001 26.2551
6 62.2781 31.7089 30.1874 6 112.8070 25.0590 27.6074
7 41.5499 30.9628 31.9451 7 32.0412 31.9243 33.0737
8 36.6626 30.8285 32.4886 8 19.9708 32.5940 35.1269
9 61.8306 30.9862 30.2188 9 72.2476 28.0401 29.5426
0
20
40
60
80
100
120
140
160
180
IMAGE 1 IMAGE 2 IMAGE 3
EXISTING METHOD
PROPOSED METHOD
6. An Efficient Approach of Segmentation and Blind Deconvolution in Image Restoration
DOI: 10.9790/0661-17614146 www.iosrjournals.org 46 | Page
V. Conclusion
In this proposed technique we have applied Blind Deconvolution Algorithm on multiple images of
different sizes having different format such as .jpg, .png, .tiff. Proposed Technique is compared with the existing
method and provides better results in terms of PSNR and MSE. Proposed technique reduces complexity of the
system than the existing method because this technique does not use any Edge Detection Method like Canny
Edge Detection, Sobel, Prewitt etc. to improve the quality of the image as compared to existing method. This
Technique is also applied on multiple segments of a single image and provides good results. But this Technique
has some limitations. It does not work for all type of Gaussian Blur added into the image.
VI. Future Work
Many other techniques of image processing can also be applied with Blind Deconvolution Algorithm to
provide improved results than the proposed technique. Methods can be developed that can work for all types of
blur added into the image. Proposed Technique consists of only nine segments of a single image. It can also be
applied on more than nine segments of images in future. Segments that are not restored properly can be further
be improved.
References
[1]. Image Segmentation and Various Segmentation Techniques – A Review By Binamrata Baral, Sandeep Gonnade, Toran Verma.
[2]. Study of Region Base Segmentation Method By Ku. Vasundhara H. Lokhande Digital Electronics,Babasaheb Naik College of
Engg. Saint Gadage Baba Amravati University, India
[3]. Digital Image Processing By Prof. P.K. Biswas. IIT, Kharagpur.
[4]. Removing Blurring From Degraded Image Using Blind Deconvolution with Canny Edge Detection Technique By Mr. A. S. Mane*
Prof. Mrs. M. M. Pawar E&TC Department, Solapur Univesity.
[5]. MRF-based Blind Image Deconvolution By Nikos Komodakis1 and Nikos Paragios Ecole des Ponts ParisTech,
nikos.komodakis@enpc.fr Ecole Centrale de Paris, nikos.paragios@ecp.fr
[6]. International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012 ISSN 2229-5518. Comparison of
Image Quality Assessment: PSNR, HVS, SSIM, UIQI By Yusra A. Y. Al-Najjar, Dr. Der Chen Soong.
[7]. International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012 ISSN 2229-5518. Comparison of
Image Quality Assessment: PSNR, HVS, SSIM, UIQI By Yusra A. Y. Al-Najjar, Dr. Der Chen Soong.