Learning Super-Resolution of Environment Matting of Transparent Objects From a Single Image
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of super-resolution of environment matting of transparent objects. In contrast to traditional methods of environment matting of transparent objects, which often require a large number of input images or complex camera setups, recent approaches using convolutional neural networks are more practical. In particular, after training, they can generate the environment mattes using a single image. However, they still do not have super-resolution capabilities. This paper first proposes an encoder-decoder network with restoration units for super-resolution environment matting, called Enhanced Transparent Object Matting Network (ETOM-Net). Then, we introduce a refinement phase to improve the details of the output further. The ETOM-Net effectively recovers lost features in the LR input images and produces visually plausible HR environment mattes and the corresponding reconstructed images, demonstrating our method’s effectiveness.
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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 | 0.001 |
| Open science | 0.001 | 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