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
this is dataset for our paper: "Large-scale Benchmark for Uncooled Infrared Image Deblurring", submitted for IEEE SIgnal Processing Letters.the abstract for paper is :Infrared images are increasingly adopted in various applications. Therefore, motion deblurring for infrared images is also receiving growing interest. However, deep learning-based deblurring techniques for infrared images have yet to be deeply studied, since there is no publicly available dataset for training and evaluating the networks. In this letter, we introduce a large-scale dynamic motion deblurring dataset for microbolometer-based uncooled infrared detectors named Uncooled Infrared Image Deblurring (UIRD), which reflects their unique blur characteristics. The dataset is generated using a combination of a cooled infrared camera, frame interpolation, IR band conversion, and a unique blur accumulation model. Benchmark results on our dataset with state-of-the-art deep learning-based deblurring algorithms are reported, and we also show the effectiveness of our dataset by showing deblurring results on real uncooled infrared images. Our dataset is publicly released to facilitate future research in this area.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.538 |
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