A pluggable single-image super-resolution algorithm based on second-order gradient loss
Convolutional neural networks for single-image super-resolution have been widely used with great success. However, most of these methods use L1 loss to guide network optimization, resulting in blurry restored images with sharp edges smoothed. This is because L1 loss limits the optimization goal of t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2023-12-01
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| Series: | BenchCouncil Transactions on Benchmarks, Standards and Evaluations |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772485923000650 |