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Open AccessArticle

Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
Xi’an Research Institute of Huawei Technologies Co., LTD, Xi’an 710075, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2593; https://doi.org/10.3390/rs11212593
Received: 28 September 2019 / Revised: 25 October 2019 / Accepted: 29 October 2019 / Published: 5 November 2019
In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem. View Full-Text
Keywords: convolutional sparse learning; image super-resolution; semi-coupled dictionary learning convolutional sparse learning; image super-resolution; semi-coupled dictionary learning
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Li, L.; Zhang, S.; Jiao, L.; Liu, F.; Yang, S.; Tang, X. Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution. Remote Sens. 2019, 11, 2593.

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