Image deblocking via joint domain learning

Wenshu Zhan; Xiaohai He; Shuhua Xiong; Chao Ren; Honggang Chen

doi:10.1117/1.JEI.27.3.033006

10 May 2018 Image deblocking via joint domain learning

Wenshu Zhan, Xiaohai He, Shuhua Xiong, Chao Ren, Honggang Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033006 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033006

Abstract

Image deblocking is a postprocessing method that aims to suppress the compression artifacts without changing existing JPEG coding standard. We propose an image deblocking method, which is based on deep convolutional neural networks. The proposed method takes full advantage of the characteristics of wavelet domain and pixel domain to restore the high-frequency information of compressed images and maintain low-frequency information, respectively. In addition, a fusion layer is employed to fuse the merits of two domains. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblocking methods in both subjective vision and objective evaluation.

Citation Download Citation

Wenshu Zhan, Xiaohai He, Shuhua Xiong, Chao Ren, and Honggang Chen "Image deblocking via joint domain learning," Journal of Electronic Imaging 27(3), 033006 (10 May 2018). https://doi.org/10.1117/1.JEI.27.3.033006

Received: 15 January 2018; Accepted: 17 April 2018; Published: 10 May 2018

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