A dual-scale convolutional neural network for super-resolution

Jing Liu; Shuai He; Yuxin Xue; Xiaoyan Zhang

doi:10.1117/12.2599278

30 June 2021 A dual-scale convolutional neural network for super-resolution

Jing Liu, Shuai He, Yuxin Xue, Xiaoyan Zhang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118781G (2021) https://doi.org/10.1117/12.2599278
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

The booming development of deep convolutional neural networks recently has made super-resolution researches achieve great progress. However, most of the existing researches only consider the super-resolution of several integer scale factors. In this paper, we propose a dual-scale convolutional neural network (DSCNN) to solve super-resolution problem of arbitrary scale factor. The magnifying module of the proposed DSCNN is designed with two chains. First, the large- scale chain learns the feature mappings from the image blocks of the low resolution (LR) to those of the high resolution (HR). The LR image blocks are the magnified image blocks with the desired size via bicubic interpolation. Second, the small-scale chain learns the feature mappings from the down-sampling image blocks to the magnified image blocks. Compared to the existing SR networks, DSCNN has two advantages: (1) it can handle the super-resolution images with arbitrary scale factors, and (2) it dynamically predicts the values rather than the weights of the interpolated pixels of HR images. The extensive experiments on widely used benchmark datasets show the superiority of the proposed DSCNN to the state-of-the-art SR methods in terms of both numerical results and visual quality.

Citation Download Citation

Jing Liu, Shuai He, Yuxin Xue, and Xiaoyan Zhang "A dual-scale convolutional neural network for super-resolution", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781G (30 June 2021); https://doi.org/10.1117/12.2599278

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