IRBFusion: Diffusion-Based Blind Image Super Resolution Using Unsupervised Learning and Bank of Restoration Networks
Bibliographic record
Abstract
Image super resolution focuses on increasing the spatial resolution of low-quality images and enhancing their visual quality. Since the image degradation process is unknown in real-life scenarios, it is crucial to perform image super resolution in a blind manner. Diffusion models have revolutionized the task of blind image super resolution in view of their powerful capability of producing realistic textures and structures. Design of the condition network is a key factor for diffusion models in providing high image super resolution performances. In this regard, we develop an effective image restoration bank by using a three-stage learning algorithm based on the idea of unsupervised learning, and feed its results, wherein visual artifacts are remarkably suppressed, to the condition network. The use of the unsupervised learning in the design of our image restoration bank guarantees that both diverse contextual information of visual signals, as well as, different degradation operations are considered for the task of blind image super resolution. Further, we guide the feature generation process of the condition network in such a way that the fidelity of the feature tensors produced for the task of image super resolution remains high. The results of extensive experiments show the superiority of our method over the state-of-the-art blind image super resolution schemes in the case of various benchmark datasets.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".