Contrast Enhancement of Poor-Quality Satellite Images Through Morphological Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a fruit of technological advancement, satellite images have been applied in many scientific fields, especially in surveillance. However, some satellite images are taken from an ultrahigh orbit in very dim situation. Data loss might occur due to the weak contrast between the dull pixels in such images, which cover a vast geographical area. Thus, it is necessary to improve the quality and contrast of satellite images. There are only a few techniques to improve the view and contrast of these images. To make matters worse, the contrast enhancement methods face many drawbacks. After estimating the brightness of each pixel, this paper integrates improved discrete wavelet transform (IDWT) with improved fuzzy C means clustering (IFCM) segment each poor-quality satellite image into multiple homogenous parts, and carries out morphological operations to enhance the contrast of the image. In addition, the proposed method was compared with traditional approaches. The results show that our method achieved the best performance in improving the quality of satellite images.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it