Deep Networks for Compressed Image Sensing
This work addresses compressed sensing for image processing, offering incremental improvements in sampling and reconstruction efficiency.
The paper tackles the challenges of designing an optimal sampling mechanism and achieving high-quality reconstruction in compressed image sensing by using deep networks to train a sampling matrix and recover images, resulting in significant quality improvements over state-of-the-art methods.
The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained superior performance. However, there still exist two important challenges within the CS theory. The first one is how to design a sampling mechanism to achieve an optimal sampling efficiency, and the second one is how to perform the reconstruction to get the highest quality to achieve an optimal signal recovery. In this paper, we try to deal with these two problems with a deep network. First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process. Then, we propose a deep network to recover the image, which imitates traditional compressed sensing reconstruction processes. Experimental results demonstrate that our deep networks based CS reconstruction method offers a very significant quality improvement compared against state of the art ones.