Self-Supervised Adaptive Illumination Estimation for Low-Light Image Enhancement
Bibliographic record
Abstract
In low-light image enhancement tasks, global structure and local texture details have different effects on illumination estimation. However, most existing works fail to effectively explore the intrinsic association within them. To effectively balance the structure-preserving and texture-smoothing for illumination maps, this paper introduces a new illumination smoothing loss and proposes a self-supervised adaptive illumination estimation network (AIE-Net). The illumination smoothing loss achieves a balance between structure-preserving and texture-smoothing mainly through L2 norm, truncated Huber, and Gaussian kernel function with color affinity. To construct AIE-Net, we introduce a local-global adaptive modulation (LGAM) module in deep feature extraction. The module allows local and global features to be adaptively fused in a spatially varying manner by predicting scaling and adding factors. Finally, we separately estimate the illumination maps for the input image and its inverted image, and then achieve exposure correction with multi-exposure fusion. Extensive experiments show that the proposed method can improve image quality under different light conditions, and has better performance and generalization ability than other methods on several datasets.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".