Enhancing Lightweight IRSR Models via Knowledge Distillation with Structural and Spectral Losses
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
For Infrared Image Super-Resolution (IRSR) technology, maintaining performance while reducing model complexity is critical for a wide range of applications. However, existing research on lightweight IRSR has been predominantly limited to modifying model architectures. This study proposes a new methodology that applies Knowledge Distillation, a representative model compression technique from supervised learning, to IRSR models. To this end, we extend the DCKD framework, previously used for RGB image super-resolution, to the IRSR domain and introduce new loss functions designed to maximize the preservation of key structural characteristics in infrared images, namely edge and spectral(Contourlet-domain) information. Through the proposed methodology, a lightweight student model trained with distilled knowledge from a high-performance, complex teacher model consistently achieved superior performance compared to the same architecture trained via standard supervised learning. This study demonstrates that Knowledge Distillation based methodology is effective for developing lightweight IRSR models and is expected to contribute to fields where high-efficiency IRSR is essential, such as real-time military surveillance, disaster response, and nocturnal reconnaissance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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