A Comprehensive Review on Sparse Representation and Compressed Perception in Optical Image Reconstruction.

This review explores the integration of sparse representation and compressed perception in optical image reconstruction. Beginning with an in-depth examination of sparse representation techniques, including dictionary learning and sparse coding, the study introduces a novel paradigm by incorporating compressed perception principles. The methodology aims to optimize efficiency, data storage, and reconstruction quality. The review delves into optimization strategies, adaptive techniques, multi-scale considerations, and real-time implementation, offering a comprehensive analysis of the current landscape. By synthesizing existing knowledge and proposing innovative approaches, this review contributes to advancing optical image reconstruction, promising future breakthroughs at the intersection of sparse representation and compressed perception. [ABSTRACT FROM AUTHOR]