Joint Self-Supervised Enhancement and Denoising of Low-Light Images
Bibliographic record
Abstract
Images taken under low-light conditions often suffer from multiple degradations such as low visibility and unknown noise. Low-light image enhancement is an important task in the field of computer vision. In order to avoid the limited number of samples in paired datasets, several self-supervised enhancement methods have been developed. However, due to the designed illumination gradient prior, most self-supervised enhancement methods based on Retinex cannot effectively constrain the illumination or suppress the amplified real noise. To solve this problem, this paper explores a joint self-supervised enhancement and denoising method for low-light image. Initially, we proposed a new regularization term, named TV-Huber, and developed an adaptive illumination estimation network (AIE-Net) to explore the intrinsic relationship between structure and texture in the illumination map. Next, the camera response model and the learned illumination are then used to enhance the contrast of low-light images and mitigate color shifts. Finally, the learned illumination maps are transformed into illumination masks. Under the assumption of independent and zero-mean noise, selective feature injection is performed on the shallow features extracted by the blind-spot network (BSN) to reduce information loss while removing unknown real noise in the dark area. Extensive experiments show that the proposed method has good generalization ability on five challenging low-light image datasets and outperforms other methods in terms of visual quality and quantitative comparison.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".