DEEP LEARNING BASED IMAGE SUPER-RESOLUTION WITH COUPLED BACKPROPAGATION

Recently deep learning methods have been applied to image super-resolution (SR). Typically, these approaches involve training a single convolutional neural network that is trained to perform resolution enhancement. We propose a new low-complexity but effective algorithm called Super-resolution with Coupled Backpropagation (SR-CBP) which builds two Coupled Auto-encoder Networks (CAN), resp. the high-resolution (HR) and low-resolution (LR) networks, that capture the features of both high and low resolution images. The two networks in CAN have the ability to self-reconstruct its own input. Specifically, SR-CBP allows joint training of the LR and HR networks to have middle layer representations that agree for a pair of images (high-resolution image and its corresponding low-resolution version). For an LR input image, its middle layer representation obtained via the trained LR network can be fed into the HR network to generate the SR result. Preliminary experiments show that SR-CBP can produce better results than state of the art single image super-resolution methods based on sparse representations. The memory and storage requirements of CAN are lesser than existing deep learning based SR methods.