Removal of remote sensing fringe noise based on image "split-recombination" method of convolution
Bibliographic record
Abstract
Remote sensing images are an important basis for humans to obtain information on the surface. However, due to the limitations of sensor industrial technology and the influence of the sensor's working environment, remote sensing images generally contain fringe noise, which seriously damages image information, and cannot be used directly. Different from many current advanced convolutional network image stripe noise removal methods which focus on optimizing the network structure, the method proposed in this paper mainly splits the input-output data of the convolutional network. Through the reorganization process, the lightweight convolutional network with fewer layers and fewer channels has a good image stripe denoising effect. However, such models using convolutional networks are only suitable for stripes with a certain width, and stripes with inappropriate widths will seriously affect the processing effect of this type of model. If strips in large-size images such as remote sensing images are removed, and a stripe is hundreds of pixels wide, the stripe removal effect of this type of model will be very weak. Therefore, this paper proposes a technique of splitting-reorganizing the image of the input-output convolutional network, so that the above-mentioned models can have an excellent removal effect on stripes of any width.
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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 | 0.000 | 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".