scholarly journals Retraction: Chen et al. Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks. Appl. Sci. 2019, 9, 2316

2021 ◽  
Vol 11 (20) ◽  
pp. 9442
Author(s):  
Yuantao Chen ◽  
Jin Wang ◽  
Xi Chen ◽  
Arun Kumar Sangaiah ◽  
Kai Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Dual Channel ◽  
Image Super Resolution ◽  
Resolution Algorithm

The published article Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks [...]

scholarly journals Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2316 ◽  
Author(s):  
Yuantao Chen ◽  
Jin Wang ◽  
Xi Chen ◽  
Arun Kumar Sangaiah ◽  
Kai Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Super Resolution ◽  
Original Image ◽  
Dual Channel ◽  
Texture Information ◽  
Deep Channel ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Shallow Channel

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.

CNF+CT: Context Network Fusion of Cascade-Trained Convolutional Neural Networks for Image Super-Resolution

2020 ◽  
Vol 6 ◽  
pp. 447-462 ◽  
Author(s):  
Haoyu Ren ◽  
Mostafa El-Khamy ◽  
Jungwon Lee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Super Resolution

scholarly journals Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution

2017 ◽  
Vol 10 (1) ◽  
pp. 165-178 ◽  
Author(s):  
Peng Ren ◽  
Wenjian Sun ◽  
Chunbo Luo ◽  
Amir Hussain
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Multi-Channel Convolutional Neural Networks for Image Super-Resolution

2017 ◽  
Vol E100.A (2) ◽  
pp. 572-580 ◽  
Author(s):  
Shinya OHTANI ◽  
Yu KATO ◽  
Nobutaka KUROKI ◽  
Tetsuya HIROSE ◽  
Masahiro NUMA
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Super Resolution

Image super-resolution with multi-channel convolutional neural networks

2016 ◽  
Author(s):  
Yu Kato ◽  
Shinya Ohtani ◽  
Nobutaka Kuroki ◽  
Tetsuya Hirose ◽  
Masahiro Numa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Super Resolution

Sentinel-3 Image Super-Resolution Using Data Fusion and Convolutional Neural Networks

2021 ◽  
Author(s):  
Rafael Fernandez ◽  
Ruben Fernandez-Beltran ◽  
Filiberto Pla
Keyword(s):  
Neural Networks ◽  
Data Fusion ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Using Data
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