Research and Analysis on Image Reconstruction Algorithm Based on Compressed Sensing

Compressed Sensing (CS) is an efficient signal processing technique that can accurately acquire and reconstruct signals at lower sampling rates exploiting the sparsity of the signals. In the field of image processing, compressed sensing image reconstruction algorithms utilize the sparse representation of images to recover the original image from small number of observations. The core of this algorithm lies in the sparse representation of the signal, the construction of the observation matrix, and the selection of the reconstruction. First, it is assumed that the original image can be sparsely represented in a certain sparse basis, such as wavelet transform or sparse dictionary. Then the sparse coefficients are randomly sampled to obtain the observations. Finally, the image is reconstructed using a compressed sensing algorithm based on the sampling data and the observation matrix. performance of the reconstruction algorithm is affected by factors such as the sparsity of the signal, the design of the observation matrix, and the selection of the reconstruction algorithm To improve the reconstruction performance, various improved algorithms have been proposed in recent years, such as the iterative hard thresholding algorithm with variable step size and the deep learning image compressed sensing algorithm.