Image De-hazing techniques for Vision based applications - A survey
Bibliographic record
Abstract
Haze is defined as a poor condition described by an iridescent atmospheric appearance that reduces clarity and visibility. The main reason for this is lot of toxic elements like dust particles, smoke in the atmosphere scattering and absorbing sun light. This poor intelligibility causes various computer vision applications to fail, including intelligent transportation, video surveillance, element recognition, and in a method to perform operations on image to get better image. There is a problem in domain of image processing wherein image recovery by various degradations is a challenge. Pictures and videos taken in outdoor environments usually suffer from reduced contrast, faded colors and with reduced visibility due to airborne particles, which directly affect image quality. This can lead to problems recognizing objects captured in blurry or still images. Several images clean up techniques have been developed to solve this problem, each with their own strengths and weaknesses, but effective image recovery is daunting task. Recently, many learning-based methods (predictive analytics and natural language processing) have tried to overcome the shortcomings of mechanical representation of properties and alleviated the challenge of efficiently reconstructing images by spending with reduce cost and comparatively reduced time. This overview delves into latest techniques for imaging with no-fog. In addition, hardware execution of many real time dehaze methods have been methodically outlined by this paper. The study done in this paper paves a way for researches in image dehazing domain as-well-as will direct them for doing further enhancement on the basis of achievements done currently.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".