Application of deep learning and compressed sensing for reconstruction of images

Abstract

Compressed sensing (CS) is a technique in signal processing which reconstructs any given signal at a rate less than that of Nyquist's' rate given that the signal is sparse and incoherent in nature. The main focus of CS is to find a random matrix which reconstructs the original signal using as few samples as possible. In recent years there has been a lot of interest in using CS to reconstruct 2D images. Several machine learning and deep learning algorithms have been proposed for finding the random matrix. However, using deep learning techniques to generate the random matrix is still an emerging concept. There are several papers exploring the concepts of deep learning with compressed sensing for images and have produced promising results. The results obtained in these papers warrant a comparison to analyze their performances against more traditional methods to determine the best approach to reconstruct images. These results are also compared with the popular JPEG and JPEG2000 codecs. This paper focuses on surveying deep learning algorithms for reconstruction of images using CS. The comparison reveals that deep learning algorithms perform significantly better than traditional methods and perform well when compared to any image compression codec like JPEG. Possible methods to improve the existing algorithms have been suggested.