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Poster
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Zhisheng Zhong · Tiancheng Shen · Yibo Yang · Zhouchen Lin · Chao Zhang

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #37
in Tue Poster Session B »

Convolutional neural networks (CNNs) have recently achieved great success in single-image super-resolution (SISR). However, these methods tend to produce over-smoothed outputs and miss some textural details. To solve these problems, we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high resolution (HR) image with better textural details in the wavelet domain. The proposed SRCliqueNet firstly extracts a set of feature maps from the low resolution (LR) image by the clique blocks group. Then we send the set of feature maps to the clique up-sampling module to reconstruct the HR image. The clique up-sampling module consists of four sub-nets which predict the high resolution wavelet coefficients of four sub-bands. Since we consider the edge feature properties of four sub-bands, the four sub-nets are connected to the others so that they can learn the coefficients of four sub-bands jointly. Finally we apply inverse discrete wavelet transform (IDWT) to the output of four sub-nets at the end of the clique up-sampling module to increase the resolution and reconstruct the HR image. Extensive quantitative and qualitative experiments on benchmark datasets show that our method achieves superior performance over the state-of-the-art methods.

Keywords: Supervised Deep Networks · Computer Vision · CNN Architectures
[ Paper] 

Author Information

Zhisheng Zhong (Peking University)
Tiancheng Shen (Peking University)
Yibo Yang (Peking University)
Zhouchen Lin (Peking University)
Chao Zhang (Peking University)

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