Multi frame super resolution technology based on deep learning and compressed sensing

Abstract

Since the birth of convolutional neural networks, the application of deep learning technology in image processing has been booming, and deep learning super-resolution technology is one of the most concerned fields. In the traditional deep learning super-resolution process, the conversion of high-resolution images to low-resolution images is usually obtained by down sampling, but when the actual image degradation does not conform to this process, the effect of the model is usually greatly reduced. Currently, single-frame input is mainly used for image super-resolution, but this operation usually leads to undesirable results in large-scale reconstruction. This article is derived from the SRMD network (a single convolutional super-resolution network with multiple degradations). On this basis, the key factors of image degradation (blur kernel and noise level) are added to the input of the model, and the measurement matrix commonly used in compressed sensing is used to generate multi-frame images. We invented the MFSR network (Multi-Frames Input Super-Resolution Network with Multiple Degradations), and achieved excellent results on the target data set.