Ultralight-Weight Three-Prior Convolutional Neural Network for Single Image Super Resolution

The task of image super resolution is crucial in many applications, such as computer vision and medical imaging. Conventionally, the task of image super resolution was carried out by formulating it as a constrained optimization problem and then solving it using suitable numerical techniques. However, after the emergence of deep neural networks, the focus of the researchers in this area has been almost entirely on designing deep convolutional neural network architectures that indeed have provided remarkable performance for the task of image super resolution. Even though unified methods of combining the two approaches has a greater potential of providing a superior performance for the task of image super resolution, with the exception of very few works, not much attention has been paid to develop such a unified method for this task. In this article, we propose a three-prior formulation of the optimization problem for image super resolution and develop an ultralight-weight convolutional neural network for its solution. The effectiveness of the proposed formulation of the optimization problem and ultralight-weight convolution neural network architecture for its solution is demonstrated through extensive experimentations of the proposed scheme on benchmark datasets and comparisons of the results with that of the other state-of-the-art ultralight-weight image super resolution networks.