Sharif Digital Repository

Sparse signal processing (SSP), as a powerful tool and an efficient alternative to traditional complete transforms, has become a focus of attention during the last decade. In this approach, we want to approximate a given signal as a linear combination of as few as possible basis signals. Each basis signal is called an atom and their collection is called a dictionary. This problem is in general difficult and belongs to the Np-hard problems; since it requires a combinatorial search. In recent years however, it has been shown both theoretically and experimentally that the sparset possible representation of a signal in an overcomplete dictionary is unique under some conditions and can be found... 

Image inpainting is the process of reconstructing lost parts of damaged images based on collected local information, or even general information, as prior knowledge. The image inpainting’s objective is to improve the damaged images, for example restoring missing pixels caused by folding, erasing background’s text in an image, removing watermarks from an image, or editing an image such as eliminating an object or a person from the image. The majority of the image inpainting algorithms approaches the problem by signal restoration from remaining samples or iterative methods to complete the damaged images. Algorithms based on samples, algorithms based on partial differential equations, and... 

Digital images degrade during transmittion via noisy channels. The goal of this thesis is to propose new methods for image recovery from random and block losses In the first part of the thesis, various techniques for image recovery from random losses will be reviewd and then a method will be proposed based on the correlation among image pixels in the spatial domain. The method is fast, efficient and robust against Gaussian noise. Also a technique will be developed for quality estimation in the recipient. The second part of the thesis devotes image recovery from block losses. After a brief survey for image inpainting techniques we intoduce the concept of image reconstruction using the... 

Nowadays millions of images are shared on social media every day , So image inpainting has become an important issue . After advent of Generative adversarial network image inpainting methodes based on deep learning has been revived and significant progress has been made . For a proper image inpainting , The inpainted image must benefit from the appropriate structure and texture in the missing regions . Therefore, in this project , an attempt is made to use a two-stage structure by using Generative adversarial network .in first stage first by using Gabor filters , the image structure is extracted and then the image structure is completed , while the second stage focuses only on the... 

The emerging field of compressed sensing has found wide-spread applications in signal processing. Exploiting the sparsity of natural image signals on basis of a set of atoms called dictionary, one can find numerous examples for applications of compressed sensing in the field of image processing. One of these interesting applications is to help recover missing samples of a damaged or lossy image signal which is also known as image inpainting. There are dozens of reasons why an image may get damaged, for instance, during data transmission, some blocks of an image (or frames of a video ) may get lost due to error in the telecommunication channel (this is known as block-loss). In this case image... 

Over the past years, translating a grayscale image to a colorful image, due to its applications in medical imaging, and restoring and colorizing old images, has been popular. The problem we are studying here is automatic grayscale image translation to a colorized image such that the colorized image appears as real as possible. Due to the large degrees of freedom in the allocation of colors to different sections of a grayscale image, this problem is extremely ill-posed. Hence, based on previous works we attempt to utilize Generative Adversarial Networks and Convolutional Neural Networks to overcome this issue. The trained model receives a grayscale image and predicts two chromatic channels of...