SASW
[ACM MM'25 Oral] A Novel Perspective on Low-Light Image Enhancement: Leveraging Artifact Regularization and Walsh-Hadamard Transform (SASW)
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[ACM MM'25 Oral] A Novel Perspective on Low-Light Image Enhancement: Leveraging Artifact Regularization and Walsh-Hadamard Transform (SASW)
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<a href="https://werring.wu.github.io">Weilin Wu*,</a> Shifan Yang, Qizhao Lin, XingHong Chen, Kunping Yang, Jing Wang and Guannan Chen <sup>✉️</sup>
<hr /> <a href="https://link.growkudos.com/1envcl9hedc">https://link.growkudos.com/1envcl9hedc</a>Abstract: Low-light image enhancement (LLIE) aims to restore low-light images to normal lighting conditions by improving their illumination and fine details, thereby facilitating efficient execution of downstream visual tasks. Traditional LLIE methods improve image quality but often introduce high-frequency artifacts, which are difficult to eliminate, hindering detail recovery and quality enhancement in LLIE. To solve this problem, we introduce a novel perspective: instead of traditional artifact suppression, sparsification-induced artifacts are repurposed as constructive regularization signals to guide detail recovery. By analyzing the impact of sparsified frequency components and their role in reconstruction artifacts, a detailed mathematical framework is presented. Specifically, we propose a novel loss function SASW-Loss which combining Sparse Artifact Similarity Loss (SAS-Loss) and Walsh-Hadamard Coefficient Loss (WHC-Loss). SAS-Loss mitigates the over-compensation of missing frequencies, helping the network recover structural details, while WHC-Loss optimizes the frequency-domain representation, restoring luminance, suppressing noise, and enhancing both structure and details. Extensive experiments show that our approach outperforms existing state-of-the-art methods, achieving superior performance in structural detail preservation and noise suppression. These results validate the effectiveness of our new perspective, which leverages sparsification artifacts to guide detail recovery, demonstrating significant improvements and robust performance across multiple models, and opening new avenues for future research.
Main FlowChart
Detail of WHT Generate Artifacts
Visual Results
1. Create Environment
The basic environment configuration can be referred to RetinexFormer, RetinexMamba, HWMNet, SNR-Net, WaveMamba and FourLLIE.
2. Prepare Dataset
Download the following datasets:
LOL-v1 Baidu Disk (code: cyh2), Google Drive
LOL-v2 Baidu Disk (code: cyh2), Google Drive
MIT-Adobe FiveK Baidu Disk (code:cyh2), Google Drive, Official
Note:
(1) Please process the raw images of the MIT Adobe FiveK dataset following the sRGB output mode or directly download and use the sRGB image pairs processed by us in the Baidu Disk (code:cyh2) and Google Drive
(2) Please download the text_list.txt from Google Drive or Baidu Disk (code: ggbh) and then put it into the folder data/SMID/SMID_Long_np/
|--data
| |--LOLv1
| | |--Train
| | | |--input
| | | | |--100.png
| | | | |--101.png
| | | | ...
| | | |--target
| | | | |--100.png
| | | | |--101.png
| | | | ...
| | |--Test
| | | |--input
| | | | |--111.png
| | | | |--146.png
| | | | ...
| | | |--target
| | | | |--111.png
| | | | |--146.png
| | | | ...
| |--LOLv2
| | |--Real_captured
| | | |--Train
| | | | |--Low
| | | | | |--00001.png
| | | | | |--00002.png
| | | | | ...
| | | | |--Normal
| | | | | |--00001.png
| | | | | |--00002.png
| | | | | ...
| | | |--Test
| | | | |--Low
| | | | | |--00690.png
| | | | | |--00691.png
| | | | | ...
| | | | |--Normal
| | | | | |--00690.png
| | | | | |--00691.png
| | | | | ...
| | |--Synthetic
| | | |--Train
| | | | |--Low
| | | | | |--r000da54ft.png
| | | | | |--r02e1abe2t.png
| | | | | ...
| | | | |--Normal
| | | | | |--r000da54ft.png
| | | | | |--r02e1abe2t.png
| | | | | ...
| | | |--Test
| | | | |--Low
| | | | | |--r00816405t.png
| | | | | |--r02189767t.png
| | | | | ...
| | | | |--Normal
| | | | | |--r00816405t.png
| | | | | |--r02189767t.png
| | | | | ...
| |--SID
| | |--short_sid2
| | | |--00001
| | | | |--00001_00_0.04s.npy
| | | | |--00001_00_0.1s.npy
| | | | |--00001_01_0.04s.npy
| | | | |--00001_01_0.1s.npy
| | | | ...
| | | |--00002
| | | | |--00002_00_0.04s.npy
| | | | |--00002_00_0.1s.npy
| | | | |--00002_01_0.04s.npy
| | | | |--00002_01_0.1s.npy
| | | | ...
| | | ...
| | |--long_sid2
| | | |--00001
| | | | |--00001_00_0.04s.npy
| | | | |--00001_00_0.1s.npy
| | | | |--00001_01_0.04s.npy
| | | | |--00001_01_0.1s.npy
| | | | ...
| | | |--00002
| | | | |--00002_00_0.04s.npy
| | | | |--00002_00_0.1s.npy
| | | | |--00002_01_0.04s.npy
| | | | |--00002_01_0.1s.npy
| | | | ...
| | | ...
| |--SMID
| | |--SMID_LQ_np
| | | |--0001
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | |--0002
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | ...
| | |--SMID_Long_np
| | | |--text_list.txt
| | | |--0001
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | |--0002
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | ...
| |--FiveK
| | |--train
| | | |--input
| | | | |--a0099-kme_264.jpg
| | | | |--a0101-kme_610.jpg
| | | | ...
| | | |--target
| | | | |--a0099-kme_264.jpg
| | | | |--a0101-kme_610.jpg
| | | | ...
| | |--test
| | | |--input
| | | | |--a4574-DSC_0038.jpg
| | | | |--a4576-DSC_0217.jpg
| | | | ...
| | | |--target
| | | | |--a4574-DSC_0038.jpg
| | | | |--a4576-DSC_0217.jpg
| | | | ...
We also provide download links for LIME, NPE, MEF, DICM, and VV datasets that have no ground truth:
Baidu Disk (code: cyh2)
or Google Drive
3. Testing
Download our models from Baidu Disk (code: 54xn) or Google Drive. Any deviation between some weights and the paper’s values stems from better results obtained in later optimizations.
We use RetinexFormer as an example. For other models, please refer to (RetinexMamba, HWMNet, SNR-Net, [WaveMamba](https://github.com/AlexZou14/Wave-Ma
