Image super-resolution by learning weighted convolutional sparse coding

Single image super-resolution (SISR) has witnessed substantial progress recently by deep learning-based methods, due to the data-driven end-to-end training. However, most existing DL-based models are built intuitively, with little thought on priors. And the lack of interpretability limits their further improvements. To avoid this, this paper presents an end-to-end trainable unfolding network which leverages both DL- and prior-based methods. Specifically, we introduce the reweighted algorithm into CSC model and solve it by learning weighted iterative soft thresholding algorithm in a convolutional manner. Based on this, we present a SISR model by learning weighted convolutional sparse coding, in which the channel attention is resorted to learn the weight. Extensive experiments demonstrate the superiority of our method to recent state-of-the-art SISR methods, in terms of both quantitative and qualitative results.