Bibliographic record
Abstract
In this paper, we propose a deconvolution method which removes the motion blur along the blurring paths. The 2-D blurred image is transformed into 1-D horizontal blurred vectors along the blurring paths. Hence, the deconvolution of stacked horizontal blurred vectors is implemented in an iterative deconvolution process by a 1-D image restoration method that saves computation time. The deconvolution process is usually implemented in the frequency domain by fast Fourier transform (FFT). The computation time of FFT used in the 1-D image restoration method for the blurred vectors is about two-fifths of that of 2-D FFT used in the common image restoration method. To get stacked horizontal blurred vectors, we first incorporate orthogonal Chebyshev polynomial into the proposed method to extract pixels along the blurring paths. Then, we expand horizontal blurred vectors smoothly to reduce boundary artifacts. At last, we add a nonquadratic regularization term to the Richardson-Lucy algorithm, which adaptively penalizes the image gradients, to avoid oversmoothing of details. Experimental results for real motion-blurred images demonstrate that our approach runs much faster than the 2-D deblurring algorithm, while achieving as high restoration accuracy and visual perception as the 2-D deconvolution algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".