Super-resolution reconstruction based on compressed sensing and deep learning model

Image super-resolution reconstruction is to use a single or a set of degraded images to produce a high resolution image, to overcome the limitation or ill-posed conditions of the image acquisition process to achieve better content visualization and scene recognition. This paper proposes a super resolution reconstruction algorithm based on the combination of compressed sensing and depth perception neural networks. The algorithm originally makes use of a double pyramid of images, built starting from the input image itself, to extract the dictionary patches, and employs a regression based method to directly map the low-resolution (LR) input patches into their related high-resolution (HR) output patches. With the integration of deep neural network architecture and the compressive sensing theory, the robustness will be enhanced. Experiments on natural images show that the proposed algorithm outperforms some of the state-of-the-art algorithm in terms of peak signal to noise ratio, mean square error and structural similarity index.