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.

这篇综述探讨了光学图像重建中稀疏表示和压缩感知的整合。该研究从深入研究稀疏表示技术（包括字典学习和稀疏编码）开始，通过结合压缩感知原理引入了一种新颖的范式。该方法旨在优化效率、数据存储和重建质量。该评论深入探讨了优化策略、自适应技术、多尺度考虑和实时实施，对当前形势进行了全面分析。通过综合现有知识并提出创新方法，这篇综述有助于推进光学图像重建，有望在稀疏表示和压缩感知的交叉点上取得突破。