LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
Bibliographic record
Abstract
Diffusion models have become a rising star in image super-resolution (SR) tasks. However, it is not trivial to apply diffusion models for light field (LF) image SR, which requires maintaining the high-quality visual appearance of each sub-aperture image (SAI) and the angular consistency between the different SAIs. This paper proposes the first diffusion-based LF image SR model, namely LFSRDiff, by incorporating the LF disentanglement mechanism and residual modeling. Specifically, we introduce a disentangled U-Net (Distg U-Net) for diffusion models, enabling improved extraction and fusion of the spatial and angular information in LF images. Furthermore, we leverage residual modeling in diffusion to learn the residual between the upsampled low-resolution and the ground truth high-resolution, which significantly accelerates model training and yields superior results compared to direct learning. Extensive experiments conducted on the five datasets demonstrate the effectiveness of our approach, which can produce realistic SR results and achieve the highest perceptual metric in terms of LPIPS. Code is publicly available at https://github.com/chaowentao/LFSRDiff.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".