We lifted 15 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| c-yn/adair | canonical | 12 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| contributions | Ran | c-yn/adair/utils/imresize.py code served (permissive licence) · get_code("b921438690ac017f") |
| crop_a_image | Ran | c-yn/adair/utils/image_io.py code served (permissive licence) · get_code("8ade57fc1fb950bb") |
| crop_img | Ran | c-yn/adair/utils/image_utils.py code served (permissive licence) · get_code("11c75b3b8bc61c21") |
| crop_patch | Ran | c-yn/adair/utils/image_utils.py code served (permissive licence) · get_code("86abcfa12b6612f0") |
| fix_scale_and_size | Ran | c-yn/adair/utils/imresize.py code served (permissive licence) · get_code("ee75635bb6c497f1") |
| get_position_from_periods | Ran | c-yn/adair/utils/schedulers.py code served (permissive licence) · get_code("cd569444547de84f") |
| imresize | Ran | c-yn/adair/utils/imresize.py code served (permissive licence) · get_code("c38287d2acea0ce0") |
| linear_warmup_decay | Ran | c-yn/adair/utils/schedulers.py code served (permissive licence) · get_code("442cb54f21dcfe50") |
| prepare_gt_img | Ran | c-yn/adair/utils/image_io.py code served (permissive licence) · get_code("e91553b51010dfd6") |
| prepare_hazy_image | Ran | c-yn/adair/utils/image_io.py code served (permissive licence) · get_code("8564be766b7cda56") |
| to_3d | Ran | c-yn/adair/net/model.py code served (permissive licence) · get_code("82a15cc1e46f7e4d") |
| to_4d | Ran | c-yn/adair/net/model.py code served (permissive licence) · get_code("b20f2a5df739a59e") |
| accuracy | Not yet run | c-yn/adair/utils/val_utils.py code served (permissive licence) · get_code("418284b6911ecae5") |
| compute_psnr_ssim | Not yet run | c-yn/adair/utils/val_utils.py code served (permissive licence) · get_code("41bf6e3c6e9bb89b") |
| slice_image2patches | Not yet run | c-yn/adair/utils/image_utils.py code served (permissive licence) · get_code("779d9bd47765629e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.14614")
get_code_for_paper("2403.14614")
have("2403.14614")
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