Abstract
Block 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 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 structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.
Highlights
The Nyquist sampling theorem requires to sample image signals with a high speed before image compression
The performance of adaptive block compressive sensing (ABCS) system presented in this article is evaluated on a number of grayscale images of 512 3 512 in size including Lenna, Barbara, Peppers, Goldhill, and Mandrill
We need to set the initial measurement times M0i according to equation (7), in which M0i is controlled by the parameter c, so the appropriate c value is first determined to ensure that the ABCS system
Summary
The Nyquist sampling theorem requires to sample image signals with a high speed before image compression. In the study by Canh et al.,[14] the image edges are used as features and adaptively allocate the measurement times for each block according to varying edge. Based on ABCS framework, we propose an ABCS coding system which uses error between blocks to allocate measuring resources.
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