Comprehensive Review on Deep Compressive Sensing Algorithms for Image Reconstruction

Image reconstruction is a fundamental problem in various fields, and deep learning has emerged as a powerful tool for achieving state-of-the-art results. This paper presents a comprehensive review of deep-learning algorithms for image reconstruction. We analyse and compare the performance of these algorithms highlighting their strengths and limitations. Furthermore, this review aims to illuminate promising research directions and inspire future innovation in deep learning-based CS. We delve into interpretability challenges, often encountered in complex deep learning models, and their implications for image reconstruction. Additionally, we address the issues of computational complexity, parameter optimisation, and the need for efficient parameter usage, particularly in resource-constrained settings. The review concludes by outlining promising future research directions, emphasizing the development of more interpretable, computationally efficient, and data-efficient deep learning algorithms for image reconstruction.