Fast high dynamic range image deghosting for arbitrary scene motion
Bibliographic record
Abstract
High Dynamic Range (HDR) images of real world scenes often suffer from ghosting artifacts caused by motion in the scene. Existing solutions to this problem typically either only address specific types of ghosting, or are very computationally expensive. We address ghosting by performing change detection on exposure-normalized images, then reducing the contribution of moving objects to the final composite on a frame-by-frame basis. Change detection is computationally advantageous and it can be applied to images exhibiting varied ghosting artifacts. We demonstrate our method both for Low Dynamic Range (LDR) and HDR images. Additional constraints based on a priori knowledge of the changing exposures apply to HDR images. We increase the stability of our approach by using recent superpixel segmentation techniques to enhance the change detection. Our solution includes a novel approach for areas that see motion throughout the capture, e.g., foliage blowing in the wind. We demonstrate the success of our approach on challenging ghosting scenarios, and that our results are comparable to existing state-of- the-art methods, while providing computational savings over these methods.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".