We lifted 10 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dsshim0125/FS-NCSR | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| deriveScaleFromSize | Ran | dsshim0125/FS-NCSR/imresize.py code served (permissive licence) · get_code("b97b2d33b67260a8") |
| deriveSizeFromScale | Ran | dsshim0125/FS-NCSR/imresize.py code served (permissive licence) · get_code("ffe34bf756a6eaa8") |
| fiFindByWildcard | Ran | dsshim0125/FS-NCSR/measure.py code served (permissive licence) · get_code("2a857e38ee87f92c") |
| imread | Ran | dsshim0125/FS-NCSR/prepare_data.py code served (permissive licence) · get_code("d4a4924746094263") |
| imread | Ran | dsshim0125/FS-NCSR/measure.py code served (permissive licence) · get_code("ff6d805555b31561") |
| random_crop | Ran | dsshim0125/FS-NCSR/prepare_data.py code served (permissive licence) · get_code("9c54ab0c75133028") |
| t | Ran | dsshim0125/FS-NCSR/measure.py code served (permissive licence) · get_code("6a8e1704755f6a34") |
| triangle | Ran | dsshim0125/FS-NCSR/imresize.py code served (permissive licence) · get_code("2722f476a816c1b9") |
| define_Flow | Not yet run | dsshim0125/FS-NCSR/models/networks.py code served (permissive licence) · get_code("6d050c5262a0b80b") |
| find_model_using_name | Not yet run | dsshim0125/FS-NCSR/models/networks.py code served (permissive licence) · get_code("7d04d276ed855e64") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based algorithm solve this ill-posedness by learning the super-resolution space and predicting diverse HR outputs. Unfortunately, the diversity of the super-resolution outputs is still unsatisfactory, and the outputs from the flow-based model usually suffer from undesired artifacts which causes low-quality outputs. In this paper, we propose FS-NCSR which produces diverse and high-quality super-resolution outputs using frequency separation and noise conditioning compared to the existing flow-based approaches. As the sharpness and high-quality detail of the image rely on its high-frequency information, FS-NCSR only estimates the high-frequency information of the high-resolution outputs without redundant low-frequency components. Through this, FS-NCSR significantly improves the diversity score without significant image quality degradation compared to the NCSR, the winner of the previous NTIRE 2021 challenge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.09679")
get_code_for_paper("2204.09679")
have("2204.09679")
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