Image dehazing with uneven illumination prior by dense residual channel attention network
Bibliographic record
Abstract
Existing dehazing methods based on convolutional neural networks estimate the transmission map by treating channel‐wise features equally, which lacks flexibility in handling different types of haze information, leading to the poor representational ability of the network. Besides, the scene lights are predicted by an even illumination prior which does not work for a real situation. To solve these problems, the authors propose a dense residual channel attention network (DRCAN) for estimating the transmission map and use an image segmentation strategy to predict scene lights. Specifically, DRCAN is built based on the proposed dense residual block (DRB) and dense residual channel attention block (DRCAB). DRB extracts the hierarchical features with increasing receptive fields. DRCAB makes the network focus on the features containing heavy haze information. After the transmission map is estimated, fuzzy partition entropy combined with graph cuts is used to segment the transmission map into scene regions covered with varying scene lights. This strategy not only considers the fuzzy intensities of the low‐contrast transmission map but also takes spatial correlation into account. Finally, a clear image is obtained by the transmission map and varying scene lights. Extensive experiments demonstrate that our method is comparable to most of existing methods.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".