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Home > Archive>Volume 30, Issue 4, 2019 >941-953. DOI:10.13328/j.cnki.jos.005663
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Deep Residual Network in Wavelet Domain for Image Super-resolution
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National Key R&D Program of China (2017YFC0803705); National Natural Science Foundation of China (61572004, 61771026); Key Project of Beijing Municipal Education Commission (KZ201910005008); Innovation Platform Construction of Qinghai Province of China (2016-ZJ-Y04)

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    Abstract:

    Single Image Super Resolution (SISR) refers to the reconstruction of high resolution images from a low resolution image. Traditional neural network methods typically perform super-resolution reconstruction in the spatial domain of an image, but these methods often ignore important details in the reconstruction process. In view of the fact that wavelet transform can separate the "rough" and "detail" features of image content, this study proposes a wavelet-based deep residual network (DRWSR). Different from other traditional convolutional neural networks, the high-resolution image (HR) is directly derived. This method uses a multi-stage learning strategy to first infer the wavelet coefficients corresponding to the high-resolution image and then reconstruct the super-resolution image (SR). In order to obtain more information, the method uses a flexible and scalable deep neural network with residual nested residuals. In addition, the proposed neural network model is optimized by combining the loss function of image space and wavelet domain. The proposed method is carried out on Set5, Set14, BSD100, Urban100, and other datasets. The experimental results show that the proposed visual effect and peak signal-to-noise ratio (PNSR) are better than the related image super-resolution method.

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段立娟,武春丽,恩擎,乔元华,张韵东,陈军成.基于小波域的深度残差网络图像超分辨率算法.软件学报,2019,30(4):941-953

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History
  • Received:April 15,2018
  • Revised:June 13,2018
  • Online: April 01,2019
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