Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Abstract

Image superresolution (ISR) is a hot topic. With the success of deep learning, the convolutional neural network-based ISR makes great progress recently. However, most state-of-the-art networks contain millions of parameters and hundreds of layers. It is difficult to apply models realistically. To solve this problem, we propose a Wavelet Sparse Coding-based Lightweight Network for Image Superresolution (WLSR). Our contributions include four aspects. Firstly, to improve the ISR performance, the WLSR utilizes the superiorities of wavelet sparse coding on ISR. Secondly, we take advantage of the dilated convolution to expand receptive fields. In this case, the filters in WLSR can acquire more information than common convolutional filters from input images. Thirdly, to deal with sparse code efficiently, we employ deformable convolution networks to obtain the convolutional kernels that concentrate on nonzero elements. Fourthly, to make WLSR uint8 quantization robust, we take advantage of the Clipped ReLU activation in the end of WLSR and balance the SR performance with running time. Experimental results indicate that, compared with state-of-the-art lightweight models, the WLSR can achieve exceptional performance with few parameters. Moreover, the WLSR contains 30 times fewer parameters than VDSR, but it works better than VDSR on the validation set.