Orthogonal Matching Algorithm
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This article discusses three crucial techniques in image processing: compressed sensing, sparse representation, and denoising reconstruction. These methods play vital roles in image compression and recovery. Compressed sensing leverages signal sparsity to represent signals with fewer data samples, typically implemented through optimization algorithms like L1-minimization. Sparse representation utilizes sparsity to express signals as linear combinations of basis vectors, often achieved using dictionary learning algorithms such as K-SVD. Denoising reconstruction employs noise reduction techniques to recover original signals, commonly implemented through thresholding operations in wavelet transforms or non-local means filtering. These three techniques find extensive applications in image processing, providing effective solutions for image manipulation and transmission.
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