Reinforcement Learning Based Dark Image Enhancement Through Color Feature Balancing
Bibliographic record
Abstract
Image enhancement refers to the process of manipulating an image to improve the visual information it contains. Images can degrade due to various factors, such as operator incompetence or low-quality picture- recording devices. The degraded images exhibit color imbalance, unwanted noise, and hue saturation disparity, especially in dark images. Furthermore, any photograph taken in low-light conditions typically falls short of attaining the desired level of visibility and essential details of the image. Most image enhancement systems depend on a predetermined set of instructions. Deep learning approaches instruct and execute certain actions during training without knowledge of the properties of individual images. Insufficient knowledge occasionally leads to the distortion of images. Conversely, an agent that utilizes Reinforcement Learning (RL) can make decisions about which actions to perform based on the input image. In order to address the aforementioned challenges, we present a novel approach in this study that use Reinforcement Learning to enhance dark images. This method accurately emulates the sequential process employed by humans during retouching. Our reinforcement learning-based agent, similar to a human expert, aims to evaluate the present state of the image prior to making any decision. Upon conducting a comprehensive analysis, we have determined that our agent, which utilizes reinforcement learning (RL), has demonstrated exceptional efficacy in maintaining the authenticity and color equilibrium of dark images. In general, the experimental results demonstrate the outstanding efficacy of the proposed approach.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".