Human Perception-based Image Enhancement Using a Deep Generative Model
Bibliographic record
Abstract
In this paper we propose a deep model for perceptual image en-hancement based on generative modeling. The proposed frame-work is inspired by the Conditional Variational AutoEncoder (CVAE)which is a well-known deep generative structure. In generativemodels, there are efficient regularizers for controlling the outputdistributions using information from input data which lead to accu-rate and visually plausible results with few parameters. Additionally,we propose to use an image quality assessment network to deter-mine the best result among those obtained by the implementedCVAEs. The proposed CVAE structure models the histogram vec-tors of different color channels and parameters of image data (i.e.,the networks do not work directly on pixel values). This configu-ration makes the proposed framework capable of using images ofdifferent sizes. Qualitative and numerical evaluations on a relateddataset compared to state-of-the-art indicate superiority of the pro-posed framework in improving image quality and content.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".