Sparse signal processing for image applications by Gao, Haoran

“…Since there are various ways to denoise noisy images or inpaint images with missing pixels such as deep-learning-based methods, the approach applying sparse signal processing techniques is still worth the attention because it exploits the intrinsic characteristic of sparsity in images. In this dissertation, the K-SVD algorithm combined with the Orthogonal Matching Pursuit (OMP) algorithm is explored and applied in image denoising and inpainting. …”
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Asymptotic performance analysis of compressed sensing reconstruction algorithm by Ong, Yan Lin

“…This paper investigates how the performance of OMP changes when the various parameter such as linear dimension n, number of measurements m and sparsity are increased.…”
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Compressed sensing for image processing by Yashwant, Mandavilli

“…This thesis work focuses on the sparsity of real-world signals. Sparse representation of images is a new measure and its applications are promising. …”
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Application of compressed sensing by Duan, Qiyu

“…Images can be damaged during acquiring from sensor or transmission in communication channel. Based on the sparsity of the signal, the compressive sensing theory can sample signal at the rate which is far below the Nyquist sampling frequency, and accurately reconstruct the original signal from sample data. …”
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Signal recovery via compressive sensing by Bangalore Ramesh Jayanth

“…The initial part of the project deals with understanding the recovery of sum of sine/cosine waves via compressive sensing using OMP algorithm by applying proper basis functions so that signal is represented with good sparsity. The second and third part of the project is aimed at recovering sine wave using different basis functions like DCT , DFT and WARPED DFT. …”
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Sparse representation and its applications to airborne platforms by Srinivasan, Abirami

“…CS relies on the inherent “sparsity”, or compressibility of a signal in some basis and operates on far fewer samples than traditional sampling schemes. …”
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Improvements to sparse signal processing in compressive sensing and other methods by Huang, Honglin

“…Different from other Orthogonal Matching Pursuit (OMP)-type algorithms, the proposed method incorporates a backtracking step to more carefully choose the reliable support set, and at the same time, it does not require the signal’s sparsity level to be known before reconstruction. Various experiments on exact sparse signal reconstruction case, noisy signal or noisy measurement approximation case, and two-dimensional (2-D) compressible signal approximation case are illustrated to show the better performance than that of other known OMP-type methods. …”
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Compressive sensing algorithms for recovery of sparse and low rank signals by Mukund Sriram Narasimhan

“…Two most common atomic structures are sparsity and low rank. A sparse signal (vector/matrix) has very few nonzero entries. …”
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Sparse signal processing and compressed sensing recovery by Sujit Kumar Sahoo

“…The works presented in this thesis focus on sparsity in the real world signals, its applications in image processing, and recovery of sparse signal from Compressed Sensing (CS) measurements. …”
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