We lifted 21 functions out of this paper's own repositories and ran 19 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 |
|---|---|---|
| YhuoyuH/RetinexMamba | canonical | 19 of 21 |
| Function | Status | Where it lives |
|---|---|---|
| calc_psnr | Ran | YhuoyuH/RetinexMamba/Enhancement/RMSE.py code served (permissive licence) · get_code("f73eaea4d5f18212") |
| calc_rmse | Ran | YhuoyuH/RetinexMamba/Enhancement/RMSE.py code served (permissive licence) · get_code("9fd9f7965e5e9672") |
| calc_ssim | Ran | YhuoyuH/RetinexMamba/Enhancement/RMSE.py code served (permissive licence) · get_code("eb63f9501a5d6798") |
| calculate_fid | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/fid.py code served (permissive licence) · get_code("1d7ce6d6dd7ff5c1") |
| calculate_psnr | Ran | YhuoyuH/RetinexMamba/Enhancement/utils.py code served (permissive licence) · get_code("d292dd965ab59629") |
| calculate_ssim | Ran | YhuoyuH/RetinexMamba/Enhancement/utils.py code served (permissive licence) · get_code("a05b184dc6c264a0") |
| compute_feature | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/niqe.py code served (permissive licence) · get_code("5170ff1c4106b27a") |
| conv | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/RetinexMamba_arch.py code served (permissive licence) · get_code("4cf5ae1af7c80889") |
| estimate_aggd_param | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/niqe.py code served (permissive licence) · get_code("a1282a48b0941f5d") |
| extract_inception_features | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/fid.py code served (permissive licence) · get_code("c9fb213c957dc82d") |
| flops_selective_scan_ref | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/SS2D_arch.py code served (permissive licence) · get_code("91788abc3267bdda") |
| get_position_from_periods | Ran | YhuoyuH/RetinexMamba/basicsr/models/lr_scheduler.py code served (permissive licence) · get_code("cd569444547de84f") |
| prepare_for_ssim | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/psnr_ssim.py code served (permissive licence) · get_code("95c6eba9350a455a") |
| prepare_for_ssim_rgb | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/psnr_ssim.py code served (permissive licence) · get_code("fb93712db7064ac4") |
| reorder_image | Ran | YhuoyuH/RetinexMamba/basicsr/metrics/metric_util.py code served (permissive licence) · get_code("95067518dc16b3e5") |
| shift_back | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/RetinexMamba_arch.py code served (permissive licence) · get_code("7b904d88704f6a90") |
| to_3d | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/IFA_arch.py code served (permissive licence) · get_code("82a15cc1e46f7e4d") |
| to_4d | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/IFA_arch.py code served (permissive licence) · get_code("b20f2a5df739a59e") |
| trunc_normal_ | Ran | YhuoyuH/RetinexMamba/basicsr/models/archs/RetinexMamba_arch.py code served (permissive licence) · get_code("afa459e9e9c02c4f") |
| PSNR | Not yet run | YhuoyuH/RetinexMamba/Enhancement/utils.py code served (permissive licence) · get_code("8eccacd101833667") |
| niqe | Not yet run | YhuoyuH/RetinexMamba/basicsr/metrics/niqe.py code served (permissive licence) · get_code("0029cb1566e47350") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In the field of low-light image enhancement, both traditional Retinex methods and advanced deep learning techniques such as Retinexformer have shown distinct advantages and limitations. Traditional Retinex methods, designed to mimic the human eye's perception of brightness and color, decompose images into illumination and reflection components but struggle with noise management and detail preservation under low light conditions. Retinexformer enhances illumination estimation through traditional self-attention mechanisms, but faces challenges with insufficient interpretability and suboptimal enhancement effects. To overcome these limitations, this paper introduces the RetinexMamba architecture. RetinexMamba not only captures the physical intuitiveness of traditional Retinex methods but also integrates the deep learning framework of Retinexformer, leveraging the computational efficiency of State Space Models (SSMs) to enhance processing speed. This architecture features innovative illumination estimators and damage restorer mechanisms that maintain image quality during enhancement. Moreover, RetinexMamba replaces the IG-MSA (Illumination-Guided Multi-Head Attention) in Retinexformer with a Fused-Attention mechanism, improving the model's interpretability. Experimental evaluations on the LOL dataset show that RetinexMamba outperforms existing deep learning approaches based on Retinex theory in both quantitative and qualitative metrics, confirming its effectiveness and superiority in enhancing low-light images.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.03349")
get_code_for_paper("2405.03349")
have("2405.03349")
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