SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Design of a residual block that provides a rich set of features while requiring only small numbers of parameters and operations is crucial for the task of single image super resolution. This is especially important in applications with limited power and storage capacity. In this paper, a new multi-domain residual block is proposed in order to generate richer set of features for the task of image super resolution. The proposed residual block consists of two feature generation modules. The first one is a spatial information processing module and the second one is a spectral information processing module. The feature maps obtained by these two feature generation modules are concatenatively fused to obtain block's output. The new residual block is used to build light-weight super resolution networks. Extensive experiments are performed using several benchmark datasets in order to evaluate the performance of the networks using the new multi-domain residual block. It is shown that the use of both the spatial and spectral features enhances the performance of the light-weight super resolution networks.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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