A Virtual-Sensor Construction Network Based on Physical Imaging for Image Super-Resolution
Bibliographic record
Abstract
Image imaging in the real world is based on physical imaging mechanisms. Existing super-resolution methods mainly focus on designing complex network structures to extract and fuse image features more effectively, but ignore the guiding role of physical imaging mechanisms for model design, and cannot mine features from a physical perspective. Inspired by the mechanism of physical imaging, we propose a novel network architecture called Virtual-Sensor Construction network (VSCNet) to simulate the sensor array inside the camera. Specifically, VSCNet first generates different splitting directions to distribute photons to construct virtual sensors, and then performs a multi-stage adaptive fine-tuning operation to fine-tune the number of photons on the virtual sensors to increase the photosensitive area and eliminate photon cross-talk, and finally converts the obtained photon distributions into RGB images. These operations can naturally be regarded as the virtual expansion of the camera's sensor array in the feature space, which makes our VSCNet bridge the physical space and feature space, and uses their complementarity to mine more effective features to improve performance. Extensive experiments on various datasets show that the proposed VSCNet achieves state-of-the-art performance with fewer parameters. Moreover, we perform experiments to validate the connection between the proposed VSCNet and the physical imaging mechanism. The implementation code is available at https://github.com/GZ-T/VSCNet.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".