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Single Image Super-Resolution Using Asynchronous Multi-Scale Network
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DOI：10.1109/LSP.2021.3110405.png)
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Abstract
En 中文
An existing multi-scale residual network (MSRN) has demonstrated its success on conducting the single image super-resolution (SISR) task. The MSRN consists of a number of multi-scale residual blocks (MSRBs), and each MSRB performs convolutions by exploiting two different sizes of windows for conducting multi-scale feature extraction. The smaller window is used to extract image features at a low scale, while the larger one is used for a high scale. To significantly reduce the number of parameters involved in the MSRB, a new feature extraction module, called the asynchronous multi-scale block (AMB), is proposed in this paper. It is based on the fact that the larger window used in the MSRB can be replaced by two smaller windows without affecting the original MSRB's function. Consequently, by replacing each MSRB with our AMB, an asynchronous multi-scale network (AMNet) is then constructed, which can yield a significant reduction on computational complexity. This means that more AMBs can be used in our AMNet to deliver superior SISR performance, while maintaining the same or comparable computational complexity to that of the MSRN. To consolidate all image features generated from all scales, a new fusion scheme, called the adaptive feature fusion block (AFFB), is proposed that weights the extracted features according to their importance for further increasing SISR's performance. Extensive experimental results have clearly shown the superiority of our proposed AMNet when compared with multiple state-of-the-arts.
Keywords:
Feature extraction
Image reconstruction
Convolution
Computational complexity
Task analysis
Superresolution
Training
Deep learning
feature extraction
feature fusion
multi-scale structure
super-resolution
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