SRNMSM: A Deep Light-Weight Image Super Resolution Network Using Multi-Scale Spatial and Morphological Feature Generating Residual Blocks
Why this work is in the frame
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Bibliographic record
Abstract
Generating features representing the textures and structures of an image is very important characteristic of a super resolution network. Morphological operations are the nonlinear mathematical operations that can process signals focusing on their structures and textures. In this paper, we propose a novel residual block to generate and process morphological features and fuse them with the conventional spatial features, in order to produce a very rich and highly representational set of residual feature maps. The proposed residual block is then used in a deep convolutional neural network for the task of image super resolution. It is shown that the capability of the proposed block in generating and using the morphological features can significantly improve the super resolution performance of a deep network. The super resolution network employing the proposed residual block is shown to outperform the state-of-the-art low-complexity image super resolution networks on various benchmark datasets.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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