Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
This addresses image quality issues for applications using compressive sensing, but it is incremental as it builds on existing deep learning approaches.
The paper tackled the block-effect problem in compressive sensing image reconstruction by proposing a fully convolutional measurement network that measures the entire scene, resulting in improved PSNR, SSIM, and visual quality compared to existing methods.
Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. In the existing methods, the scene is measured block by block due to the high computational complexity. This results in block-effect of the recovered images. In this paper, we propose a fully convolutional measurement network, where the scene is measured as a whole. The proposed method powerfully removes the block-effect since the structure information of scene images is preserved. To make the measure more flexible, the measurement and the recovery parts are jointly trained. From the experiments, it is shown that the results by the proposed method outperforms those by the existing methods in PSNR, SSIM, and visual effect.