| Autor: |
Honnang Alao, Tae Sung Kim, Kyujoong Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 104567-104576 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3210694 |
| Popis: |
The implementation of deep convolutional neural networks (CNN) in single image super-resolution (SISR) has been successful at improving restoration quality. However, due to analysis made in previous works, it is observed that missing details in low-resolution (LR) images mostly exist in high-frequency regions. Since CNN operates all regions of the low-resolution (LR) image equally, the computation redundancy is observed in the low-frequency area. We generate a gradient-based binary mask (decision mask) to discriminate the high-frequency areas from the low-frequency areas and apply two kinds of convolution to them separately. We propose an Area-Specific CNN (ASCNN) for super-resolution. It consists of high parameter convolutions and low parameter convolutions to process the high-frequency areas and low-frequency areas separately, which efficiently reduces the FLOPs (floating-point operation) while maintaining restoration quality. The settings for reduction are configurable and experimental results show that ASCNN achieves state-of-the-art performance with FLOPs reduction up to 40.1% / 37.0% / 34.0% for $\times 2/\times 3/\times 4$ scale factors. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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