An Improved CycleGAN-Based Model for Low-Light Image Enhancement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The low-light image enhancement is a challenging and hot research issue in the image processing field. In order to enhance the quality of low-light images to obtain full structure and details, many low-light image enhancement algorithms have been proposed and deep learning-based methods have achieved great success in this field. However, most of the deep learning methods require paired training data, which is difficult to obtain. And the overall visual quality of the enhanced image is still not very satisfying. To deal with these problems, an unsupervised low-light image enhancement model based on an improved Cycle-Consistent Generative Adversarial Networks (CycleGAN) is proposed in this paper. In the proposed model, a low-light enhancement generator of the CycleGAN network is constructed based on an improved U-Net structure, and the adaptive instance normalization (AdaIN) is designed to learn the style of the normal light image. In particular, a detail enhancement method based on multi-layer guided filtering is added to the proposed model, which can improve the quality and visual pleasantness of image enhancement. In addition, a joint training strategy based on structural similarity is presented, to strengthen the constraints on generating more realistic and natural images. At last, extensive experiments are conducted and the results show that the proposed method can accomplish the task of transferring low-light images to normal light and outperform the state-of-the-art approaches in various metrics of visual quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it