Unsupervised Single-Image Reflection Removal
Bibliographic record
Abstract
Reflections often degrade the quality of images by obstructing the background scenes. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised learning models. These models require an extensive number of image pairs of the same scenes with and without reflections to perform well. However, collecting such image pairs is challenging and costly. Thus, most current supervised models are trained on small datasets that cannot cover the numerous possibilities of real-life images with reflections. In this paper, we propose an unsupervised method for single-image reflection removal. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional neural networks on a target image to generate two exclusive background and reflection layers. In particular, we design a network model that embeds semantic features extracted from the input image and utilizes these features in the separation of the background layer from the reflection layer. We show through objective and subjective studies on benchmark datasets that the proposed method substantially outperforms current methods in the literature. The proposed method does not require large datasets for training, removes reflections from single images, and does not impose impractical constraints on the input images.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".