Filter fusion for image enhancement using reinforcement learning
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
A new approach to image enhancement based on fusion of a number of filters using a reinforcement learning scheme is presented. In most applications the result of applying a single filter is usually unsatisfactory. Appropriate fusion of the results of several different filters, such as median, local average, sharpening, and Wiener filters, can resolve this difficulty. Many different techniques already exist in literatures. In this work, a reinforcement-learning agent will be proposed. During learning, the agent takes some actions (i.e., different weights for filters) to change its environment (the image quality). Reinforcement is provided by a scalar evaluation determined subjectively by the user. The approach has several advantages. The user interaction eliminates the need for objective image quality measures. No formal user model is required. Finally, no training data is necessary. The paper describes the implementation and evaluation of a global reinforced adjustment of the weights of the different filters.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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