Adaptive compressive sensing of images using VSBCD algorithm and improvement

Abstract

Compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide an image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structural complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors To overcome negative effects, we propose a versatile square based compressive detecting (VSBCD) system based on spatial entropy. Spatial entropy measures the amount of information, which is used to allocate measuring resources to various regions The reconstructed image should be better in both PSNR and bandwidth. Medical field especially in MRI scanning, compressive sensing can be utilized for less scanning time.