Journal
APPLIED SCIENCES-BASEL
Volume 9, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/app9112316
Keywords
super-resolution; dual-channel; residual block; convolutional kernel parameter; long-term and short-term memory blocks
Categories
Funding
- National Natural Science Foundation of China [61811530332, 61811540410]
- Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation [2015TP1005]
- Open Research Fund of Hunan Provincial Science and Technology Innovation Platform of Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration [2018DT04]
- Changsha Science and Technology Planning [KQ1703018, KQ1706064, KQ1703018-01, KQ1703018-04]
- Research Foundation of Education Bureau of Hunan Province [17A007, 16A008]
- Changsha Industrial Science and Technology Commissioner [2017-7]
- Junior Faculty Development Program Project of Changsha University of Science and Technology [2019QJCZ011]
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For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution algorithm based on dual-channel convolutional neural networks (DCCNN). The novel structure of the network model was divided into a deep channel and a shallow channel. The deep channel was used to extract the detailed texture information from the original image, while the shallow channel was mainly used to recover the overall outline of the original image. Firstly, the residual block was adjusted in the feature extraction stage, and the nonlinear mapping ability of the network was enhanced. The feature mapping dimension was reduced, and the effective features of the image were obtained. In the up-sampling stage, the parameters of the deconvolutional kernel were adjusted, and high-frequency signal loss was decreased. The high-resolution feature space could be rebuilt recursively using long-term and short-term memory blocks during the reconstruction stage, further enhancing the recovery of texture information. Secondly, the convolutional kernel was adjusted in the shallow channel to reduce the parameters, ensuring that the overall outline of the image was restored and that the network converged rapidly. Finally, the dual-channel loss function was jointly optimized to enhance the feature-fitting ability in order to obtain the final high-resolution image output. Using the improved algorithm, the network converged more rapidly, the image edge and texture reconstruction effect were obviously improved, and the Peak Signal-to-Noise Ratio (PSNR) and structural similarity were also superior to those of other solutions.
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