New method for detecting and removing random-valued impulse noise from images
Bibliographic record
Abstract
The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".