Image compressive sensing reconstruction system and method utilizing weighted structural group sparse regulation

Abstract

The invention discloses an image compressive sensing reconstruction system and method utilizing weighted structural group sparse regulations, and relates to the image recovery technology field. In the system, an initialization module, a routing selection module, a regularization mean square error minimum module and an image filtering processing module interact in sequence, and the image filtering processing module and the routing selection module interact. The image filtering processing module comprises an image overlap partitioning unit, an image similar block generation unit, a transformation domain weighted soft threshold filtering unit and an image block pixel domain averaging unit which interact in order. In the first stage, the image compressive sensing reconstruction method is adopted to obtain a reconstructed initial evaluation value of a compressive sensing image. In the second stage, on the basis of non-local similarity of the image, the reconstruction quality of the compressive sensing image is improved through multiple times of iteration by adopting optimization of weighted structural group sparse expressed regularization. Image textures and recovery effects of image edges can be improved, and the reconstruction quality of the compressive sensing image can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of image restoration, in particular to an image compression sensing reconstruction system and method using weighted structure group sparse rules. Background technique [0002] The Compressive Sensing (CS) theory proposed by Donoho et al. as early as 2006 breaks through the constraints of the traditional Nyquist sampling theorem, and can realize the dimensionality reduction sampling of sparse signals, thereby realizing signal sampling and compression at the same time. Finish. The reconstruction of compressed sensing images aims to restore the reduced-dimensional sampling values ​​obtained by image compression sensing and restore the original image. Image compression sensing has broad application prospects in remote sensing images, medical imaging and other fields. [0003] Although traditional image compression sensing reconstruction methods based on sparse transforms (such as discrete cosine transforms, di...

AI Technical Summary

Problems solved by technology

However, in the past, these methods mainly used the same threshold filtering for different coefficients in the transform domain to improve the quality of the reconstructed image for similar block groups in the image. Satisfactory reconstruction effect

Images