A survey on image compressive sensing: From classical theory to the latest explicable deep learning

Lijun Zhao, Yufeng Zhang, Xinlu Wang, Jinjing Zhang, Huihui Bai, Anhong Wang

Abstract: Deep learning has achieved significant advancements in both low-level and high-level computer vision tasks, which can also drive the development of an essential research field of Image Compressive Sensing (ICS) today and in the future. Nowadays model-inspired ICS reconstruction methods have gained considerable attention from researchers, resulting in numerous new developments. However, existing literature lacks a comprehensive summary of these advancements. To revitalize the field of ICS, it is crucial to summarize them to provide valuable insights for various other fields and practical applications. Thus, this article first looks back on foundational theories of ICS, including signal sparse representation, sampling and reconstruction. Next, we summarize different types of measurement matrices for sampling, which include learnable/non-learnable measurement matrix, uniform/non-uniform measurement matrix. Then, we provide a detailed review of ICS reconstruction, covering traditional optimization reconstruction methods, inexplicable reconstruction methods and explainable reconstruction methods as well as Transformer-based reconstruction methods, which will help readers quickly grasp the history of ICS development. We also evaluate several representative ICS reconstruction methods on publicly available datasets, comparing their performance and computational complexities to highlight their strengths and weaknesses. Finally, we conclude this paper and their future opportunities and challenges are prospected. All related materials can be found at https://github.com/mdcnn/CS-Survey.

External IDs:dblp:journals/pr/ZhaoZWZBW26