IllumiCurveNet: Low-Light Image Enhancement of Lunar Permanently Shadowed Regions Using a Self-Guided Loss Framework
Bibliographic record
Abstract
Lunar Permanently Shadowed Regions (PSRs) are areas near the Moon’s poles that remain in perpetual darkness due to its axial tilt. Obtaining clear and high-quality images of these regions are crucial for exploring lunar surface and detecting valuable minerals. However, due to the absence of illumination, PSR images often suffer from low visibility, poor contrast, and elevated noise levels, making their enhancement a significant challenge. To overcome these challenges, this paper introduces IllumiCurveNet, a novel framework leveraging an encoder-decoder architecture with spatial attention, dilated convolutions, and adaptive gamma correction for illuminance optimization. It uses the proposed Self-Guided Loss Framework that integrates the novel texture preservation and contrast enhancement losses, along with exposure control, spatial consistency, color consistency, and total variation losses, enabling robust enhancement without paired training data. IllumiCurveNet achieves state-of-the-art performance on PSR images with no-reference image quality metrics, surpassing other zero-shot methods. The results highlight IllumiCurveNet’s potential for applications in lunar mapping, rover navigation, and resource analysis, advancing visual perception in unlit extraterrestrial environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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 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".