The Image Super-Resolution Algorithm Based on the Dense Space Attention Network (July 2020)

The deep learning technique has been recently used in the image super-resolution. When the deep learning network is too deep, it is difficult to train this network to make it converge. Furthermore, the problem of gradient loss occurs for a very deep network, which makes the gradient at front layers...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Duanmu, Chunjiang, Zhu, Junjie
Format: Article
Language:English
Subjects:
Algorithms
Convolution
convolution neural network
Deep learning
Feature extraction
Image edge detection
image processing
Image reconstruction
Image resolution
Machine learning
single image super-resolution
Spatial resolution
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Summary:The deep learning technique has been recently used in the image super-resolution. When the deep learning network is too deep, it is difficult to train this network to make it converge. Furthermore, the problem of gradient loss occurs for a very deep network, which makes the gradient at front layers go to zero, and thus it is impossible to train these layers. Current deep learning in image super-resolution has the following two shortcomings. One is that the features of each convolutional layer are not fully utilized. The other is that the low-resolution input features, containing rich low-frequency information, are treated equally for each channel, and not well utilized. Therefore, a DSAB (dense spatial attention block) is proposed as a basic structure for the network. The features extracted from a DSAB can be treated as a feature matrix. All the feature matrices extracted through the DSABs are concated in the row direction, and are then the inputs for a convolution layer with the kernel size of 3 by 3. The whole network of the proposed algorithm is then given and described in details. The implementation details of the algorithm is then described. The data set of DIV2K, which contains 900 high-resolution images, is used for training of the network. The test images are from Set5 and Set14, as in other papers. Experimental results show that the proposed network model is better than several other representative methods, and the edges of the reconstructed image are clearer and sharper.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3013401