EFFRBNet: A Deep Super Resolution Network using Edge-Assisted Feature Fusion Residual Blocks
Bibliographic record
Abstract
Deep convolutional networks provide very high quality super resolution images through a learning process by a nonlinear end-to-end mapping between low and high resolution images. Many of the state-of-the-art super resolution networks employ residual blocks in their network architectures, where in each residual block the high frequency residual signals are added to the feature maps input to the block. In this paper, a new residual block is proposed for the problem of image super resolution. The proposed residual block consists of three modules, namely, feature transformation module, nonlinear edge extraction module and feature fusion module. The feature transformation module produces high frequency residual signals and the nonlinear edge extraction module extracts the edges of the features input to the block. These generated high frequency features are then fused using the feature fusion module in order to produce a very rich set of high frequency residual features. The performance of the super resolution network using the proposed residual block is compared with that of the state-of-the-art light-weight super resolution schemes on four benchmark datasets. It is shown that the proposed super resolution scheme outperforms the state-of-the-art light-weight super resolution networks, when both the performance and number of parameters of the network are simultaneously taken into consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".