Compressive Sensing Image Reconstruction Algorithm

In this article, I present my custom compressive sensing image reconstruction algorithm. This algorithm can reconstruct images through direct observation of the original 2D signal. The main characteristic of this approach is its ability to restore high-quality images using relatively small amounts of data, making it widely applicable in the field of image processing. My implementation is based on advanced mathematical principles and numerical computation methods, incorporating key functions for signal sampling, sparse representation, and optimization reconstruction. Through numerous experiments and parameter adjustments, the algorithm has demonstrated excellent performance. The code implementation includes efficient matrix operations for measurement collection and employs optimization algorithms such as L1-minimization for the reconstruction process. I hope to share my methodology and experience through this article, contributing to the advancement of image processing technologies.