Deep skip connection and multi-deconvolution network for single image super-resolution

Shiyu Hu; Muwei Jian; Guodong Wang; Yanjie Wang; Zhenkuan Pan; Kin-Man Lam

doi:10.1117/12.2567030

1 June 2020 Deep skip connection and multi-deconvolution network for single image super-resolution

Shiyu Hu, Muwei Jian, Guodong Wang, Yanjie Wang, Zhenkuan Pan, Kin-Man Lam

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115152X (2020) https://doi.org/10.1117/12.2567030
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

In this paper, we propose an efficient single image super-resolution (SR) method for multi-scale image texture recovery, based on Deep Skip Connection and Multi-Deconvolution Network. Our proposed method focuses on enhancing the expression capability of the convolutional neural network, so as to significantly improve the accuracy of the reconstructed higher-resolution texture details in images. The use of deep skip connection (DSC) can make full use of low-level information with the rich deep features. The multi-deconvolution layers (MDL) introduced can decrease the feature dimension, so this can reduce the computation required, caused by deepening the number of layers. All these features can reconstruct high-quality SR images. Experiment results show that our proposed method achieves state-of-the- art performance.

Citation Download Citation

Shiyu Hu, Muwei Jian, Guodong Wang, Yanjie Wang, Zhenkuan Pan, and Kin-Man Lam "Deep skip connection and multi-deconvolution network for single image super-resolution", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115152X (1 June 2020); https://doi.org/10.1117/12.2567030

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KEYWORDS

Super resolution

Image processing

Image restoration

Image quality

Visualization

Convolution

Convolutional neural networks

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