Compressive Sensing-Based Image Reconstruction Technology

In the field of computer vision, image processing technology has always been a critical research direction. In recent years, compressive sensing technology has gained widespread application in image processing domains. Based on this technology, we developed an image reconstruction program that performs image compression followed by reconstruction using compressive sensing algorithms, thereby improving both the quality and speed of image reconstruction. The implementation typically involves sparse representation using wavelets or discrete cosine transforms, followed by reconstruction through L1-norm optimization algorithms like basis pursuit or iterative thresholding. This program can be applied to medical image reconstruction, high-speed photography, and drone image processing, while providing more efficient tools and technical support for related research fields and applications. Key functions include sparse sampling matrix generation, optimization solver implementation, and quality evaluation metrics calculation.