Low-Light Image Enhancement via Feature Restoration
Bibliographic record
Abstract
Besides poor visibility, under-exposed images often suffer from severe noise and color distortion. Most existing Retinex-based methods deal with the noise and color distortion via some careful designs to denoising and/or color correction. In this paper, we propose a simple yet effective network from the perspective of feature map restoration to mitigate such issues without constructing any explicit modules. More concretely, we build an encoder-decoder network to reconstruct images, while a feature restoration subnet is introduced to transform the features of low-light images to those of corresponding clear ones. The enhanced images are consequently acquired through assembling the restored features by the decoder, in which, the noise and possible color distortion can be greatly remedied. Extensive experiments on widely-used datasets are conducted to validate the superiority of our design over other state-of-the-art alternatives both quantitatively and qualitatively. Our code is available at https://github.com/YaN9-Y/FRLIE.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".