Full Image Recover for Block-Based Compressive Sensing
This addresses image quality issues in compressive sensing applications, offering an incremental improvement over prior block-by-block methods.
The paper tackles the block effect problem in block-based compressive sensing image reconstruction by proposing a CNN-based network that reconstructs the full image from all measurements simultaneously, removing block effects and outperforming existing methods by 1.8 dB.
Recent years, compressive sensing (CS) has improved greatly for the application of deep learning technology. For convenience, the input image is usually measured and reconstructed block by block. This usually causes block effect in reconstructed images. In this paper, we present a novel CNN-based network to solve this problem. In measurement part, the input image is adaptively measured block by block to acquire a group of measurements. While in reconstruction part, all the measurements from one image are used to reconstruct the full image at the same time. Different from previous method recovering block by block, the structure information destroyed in measurement part is recovered in our framework. Block effect is removed accordingly. We train the proposed framework by mean square error (MSE) loss function. Experiments show that there is no block effect at all in the proposed method. And our results outperform 1.8 dB compared with existing methods.