Ultralight-Weight Three-Prior Convolutional Neural Network for Single Image Super Resolution
Bibliographic record
Abstract
The task of image super resolution is crucial in many applications, such as computer vision and medical imaging. Conventionally, the task of image super resolution was carried out by formulating it as a constrained optimization problem and then solving it using suitable numerical techniques. However, after the emergence of deep neural networks, the focus of the researchers in this area has been almost entirely on designing deep convolutional neural network architectures that indeed have provided remarkable performance for the task of image super resolution. Even though unified methods of combining the two approaches has a greater potential of providing a superior performance for the task of image super resolution, with the exception of very few works, not much attention has been paid to develop such a unified method for this task. In this article, we propose a three-prior formulation of the optimization problem for image super resolution and develop an ultralight-weight convolutional neural network for its solution. The effectiveness of the proposed formulation of the optimization problem and ultralight-weight convolution neural network architecture for its solution is demonstrated through extensive experimentations of the proposed scheme on benchmark datasets and comparisons of the results with that of the other state-of-the-art ultralight-weight image super resolution networks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".