كيف تنشر كتاباً؟
في بداية حياتك ككاتب، يتعامل معك القارئ بحذر، أو بإنكار في أحياناً كثيرة، وحينما يرى كتابك للوهلة الأولى ربما يقول في نفسه “ومن هذا الذي يريد مني أن أحبه؟”، في هذه المرحلة يجب عليك ألا تجبر القارئ على أن يحبك؛ فكما تعلم: “الحب لا يأتي عنوة”، لكن بدلاً من ذلك عليك أن تجعله يهابك.
كيف تنشر كتاباً؟
في بداية حياتك ككاتب، يتعامل معك القارئ بحذر، أو بإنكار في أحياناً كثيرة، وحينما يرى كتابك للوهلة الأولى ربما يقول في نفسه “ومن هذا الذي يريد مني أن أحبه؟”، في هذه المرحلة يجب عليك ألا تجبر القارئ على أن يحبك؛ فكما تعلم: “الحب لا يأتي عنوة”، لكن بدلاً من ذلك عليك أن تجعله يهابك.
This document provides goals and instructions for a course project on image restoration. The goals are to learn how to restore blurred images using the VEGA program and to evaluate restoration quality using measures like PSNR, BSNR and ISNR. Students are asked to choose a blurred image, restore it using different filters in VEGA, compare the results, optimize parameters, and write a report on the PSNR measurements. Extra credit involves designing Matlab functions to calculate BSNR and ISNR and including these in the evaluation report. Restored images and the report are to be saved in a designated project subfolder.
This document provides instructions for a course project to learn how to filter periodic noise from corrupted images using the Fourier transform. Students are asked to take two corrupted images, Lena_Y_Halftone.tif and Lena_Y_Grid.tif, and use Fourier Spectrum Median Filtering and Gaussian Median Filtering in the VEGA program to reduce or remove the noise. Students should find the best filter parameters and evaluate the filtered results compared to the original images using PSNR/RMSE metrics. A brief report on the findings and PSNR/RMSE values for each image is to be submitted.
This project tests the efficiency of unsharp masking and edge detection techniques for enhancing image details. Students are asked to:
1. Enhance image details using mean and median filters of varying window sizes.
2. Detect edges using various edge detection methods and filters.
3. Combine edge images with originals using weights to further enhance details.
4. Prepare a report comparing enhanced details from different detection methods.
This document outlines the goals of a course project on order statistic filters and computer vision. The project involves:
1) Designing a Matlab function to perform rank order epsilon (EV) filtering on noisy images using a 3x3 or arbitrary size filter window.
2) Adding Gaussian noise to images from a previous project and filtering the noisy images using the designed function.
3) Calculating the standard deviation of noisy images to use as the EV parameter and accounting for border effects when filtering.
4) Finding the RMSE and PSNR for filtered images and comparing to results from linear filters used previously.
5) Preparing a technical report with RMSE/PSNR values and conclusions.
This project aims to detect and filter impulse noise from images using differential rank impulse detection followed by median filtering. Specifically, the student must design a Matlab function that takes an image, rank interval length r, and threshold s as inputs to detect noisy pixels and apply median filtering only to those pixels. The function should handle border effects using mirroring. The student will then add random impulse noise to a test image, filter it using their function with different parameter values, and report the best filtering result based on RMSE/PSNR metrics in a brief technical report.
This project involves comparing the efficiency of spatial domain linear filters for image processing. Functions will be written for calculating error metrics between images, adding Gaussian noise to images, and applying 3x3 linear filters to images. A script will utilize these functions to measure error between clean, noisy, and filtered versions of test images. Two linear filters will be applied to images with 0.2 and 0.4 standard deviation noise added. Results will be analyzed to determine the optimal linear filter and noise level for each test image. Reports will justify conclusions and include image files and a table of results.
This document outlines the goals and steps for a course project in image processing and computer vision. The project involves writing Matlab functions to analyze image statistics, create histograms, perform histogram equalization, and linear contrast correction. A script will then use these functions to analyze two images by measuring their statistics, enhancing contrast through histogram equalization and linear correction, measuring the enhanced images' statistics and plotting histograms. The results will be saved in a project subfolder.
This document outlines the goals of a course project to design Matlab functions for simulating and filtering impulse noise in images. The functions will generate random impulse noise and salt-and-pepper noise in images at different corruption rates, and perform median filtering on noisy images. The project involves corrupting test images with noise, filtering the images, calculating RMSE and PSNR metrics, and reporting the results.
This document outlines the goals of a course project on color image processing and BM3D filtering. It provides 4 noisy test images - 2 with color Gaussian noise, 1 with color impulse noise, and 1 with monochromatic noise. It instructs the student to apply rank-order filtering to suppress Gaussian noise and median filtering to suppress impulse noise. It also instructs the student to process color noise by filtering individual color channels and compare to filtering the luminosity channel only. Bonus credit is offered for using custom Matlab code or applying the BM3D filter to individual channels. The student is asked to prepare a report on PSNR measurements and submit results.
Localization and recognition are tasks of computer vision that involve identifying the spatial coordinates of an object (localization) and determining what type of object it is (recognition). Cross-correlation is a classical tool that can be used to solve localization and recognition problems by measuring the similarity between two signals or images. It works by calculating the dot product of one signal or image while sliding it over another, with a larger value indicating a more similar match. The Fourier transform of the cross-correlation is equal to the product of the Fourier transforms of the two inputs. This property allows for an efficient computation of cross-correlation using inverse Fourier transforms. If an image is contained within another, their cross-correlation will have a strong maximum at the coordinates where
Digital color images are characterized by 3 or 4 dimensional vectors representing pixels in a color space model like RGB or CMYK. RGB uses additive color mixing of red, green and blue light, while CMYK uses subtractive color mixing of cyan, magenta, yellow and black inks. Color images can be processed by manipulating individual color channels separately or by extracting and processing luminosity from the RGB channels. Transforming between RGB and luminosity (YUV) space allows separate processing and recombination of luminosity and chromatic information. YUV space is also used in JPEG compression by applying higher compression rates to the chromatic channels U and V.
This document discusses methods for restoring blurred images, including modeling image degradation using convolution with a point spread function in the spatial and frequency domains. Common point spread functions like Gaussian and motion blur are described. Methods for solving the deconvolution problem to restore blurred images are presented, including inverse filtering, Wiener filtering, regularization filtering, and evaluating the quality of restored images using metrics like PSNR, BSNR, and ISNR.
BM3D is a denoising algorithm that uses block matching and collaborative filtering in 3D transform domains. It finds similar 2D image fragments, stacks them into 3D groups, applies a 3D transformation, shrinks the transform coefficients to attenuate noise, and inverse transforms to estimate denoised fragments. BM3D improves on previous methods by exploiting inter-fragment correlation both within and between similar image blocks grouped together. It provides multiple estimates for each pixel by processing overlapping blocks, then fuses the estimates to produce the final denoised image.
Frequency domain filtering involves modifying the Fourier transform of an image by multiplying it with a filter function. Periodic noise appears as unusually high magnitudes in the spectral coefficients corresponding to the noise frequency. This noise can be reduced or removed in the frequency domain by correcting these coefficients using techniques like thresholding or median filtering in local neighborhoods. Filtering in blocks instead of the whole image can better handle non-uniform quasi-periodic noise.
The document discusses frequency domain processing and the Fourier transform. It defines key concepts such as:
- The frequency domain represents how much of a signal lies within different frequency bands, while the time domain shows how a signal changes over time.
- The Fourier transform provides the frequency domain representation of a signal and is used to analyze signals with respect to frequency. Its inverse transform reconstructs the original signal.
- The Fourier transform decomposes a signal into orthogonal sine and cosine waves of different frequencies, showing the contribution of each frequency component. This representation is important for signal processing tasks like filtering.
This document discusses edge detection techniques in digital image processing. It begins by defining edges as areas of abrupt intensity change and explains that differentiation is used to detect edges locally. It then discusses the importance of edge detection for image segmentation and defect detection. The document goes on to explain that edge detection involves high-pass spatial domain filtering to eliminate low and medium frequencies while passing or enhancing high frequencies. It covers various first and second order derivative approaches to edge detection, including Roberts, Prewitt, Sobel, and Laplacian methods. Thresholding and boolean filtering techniques are also summarized for precise edge detection.
This document discusses sharpening filters used in digital image processing. It describes two types of sharpening filters: high-pass filters that enhance high frequencies and frequency correcting filters that enhance high and medium frequencies. Unsharp masking is presented as a classical frequency correcting filter that subtracts a blurred version of an image from the original to highlight transitions. Practical implementations of linear and nonlinear unsharp masking filters are shown for local and global frequency correction. Parameters for controlling the level of sharpening and frequency enhancement are also discussed.
The document discusses various nonlinear spatial domain filters for image processing, including median filters, threshold boolean filters, and rank-order filters.
The median filter replaces pixel intensities with the median value within a local window. It is highly effective at removing impulse noise while preserving edges. Threshold boolean filtering processes separate binary slices of an image before recombining, allowing different processing of each intensity level. Rank-order filters analyze the variational series within a window to perform nonlinear averaging, with examples including the median and rank-order EV filters.
This document discusses linear spatial domain filtering and optimal linear filtering of digital images. It introduces linear spatial filtering using a filter kernel and defines optimal filtering as minimizing the expected square error between the estimated and original images. An optimal linear filter kernel can be found by solving a Wiener-Hopf system of equations involving the image and filter autocorrelations and crosscorrelation. Common noise models - Gaussian, impulse, salt-and-pepper, bipolar, and random impulse noise - are also summarized along with their probability density functions.
Local neighborhood processing is a common technique in spatial domain image filtering. It involves defining a neighborhood around each pixel and applying an operation to the pixel values within the neighborhood. Common examples are mean and weighted mean filters, which average pixel values to reduce noise. Mean filters replace each pixel value with the average of neighboring pixels. Weighted mean filters assign more importance to central pixels and horizontally/vertically adjacent pixels compared to diagonal neighbors. Neighborhood processing is implemented by defining a filter kernel that specifies the operation and applying it to each pixel location.