Color Balanced Histogram Equalization for Image Enhancement
Bibliographic record
Abstract
Recovering a clear image solely from a hazy input image is a challenging task. Moreover, a hazy image can drastically impact the performance of many subsequent high-level computer vision tasks, such as object detection and recognition. In this study, we propose a novel image dehazing method: Color Balancing and Histogram Equalization (CBHE). The method is designed with an aim to merge it with existing object detector models like Faster RCNN [1] and improve the accuracy of object detection under poor visibility. In this method, color balancing and histogram equalization along with image processing techniques have been applied for dehazing. We used the dataset from the UG2+ challenge Track 2 competition called Realistic Single Image Dehazing(RESIDE) - STANDARD that comprised of a diverse set of both synthetic and real-world images. Experimental results on both indoor and outdoor test datasets demonstrate a large improvement in the object detection performance compared to existing techniques when the dehazed image is merged with a pre-trained object detector.
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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 | 0.000 | 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 | 0.000 |
| 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".