Image Processing with Compressed Sensing and Wavelet Transform

In the field of digital image processing, the utilization of compressed sensing and wavelet transform has become a widely adopted methodology. Image fusion represents a prominent application area, where the primary objective is to combine multiple source images into a single composite image to extract richer and more comprehensive information. Through image fusion techniques, enhancements in image sharpness, contrast, and detail resolution can be achieved, ultimately improving overall image quality. Typical implementations involve wavelet decomposition for multi-resolution analysis, compressed sensing algorithms for sparse signal reconstruction, and fusion rules (such as weighted averaging or maximum selection) for coefficient integration. Consequently, programs based on compressed sensing and wavelet transform for image processing and fusion have evolved into indispensable tools in digital image processing, with key functions including wavelet decomposition/reconstruction (e.g., using 'wavedec2' and 'waverec2' in MATLAB) and optimization algorithms for sparse recovery (e.g., orthogonal matching pursuit).