Adaptive Depth Guided Image Completion for Structure and Texture Synthesis
Bibliographic record
Abstract
The problem of synthesis of missing image parts represents an interesting and challenging area of image processing and computer vision with significant potential. This thesis, focuses on an adaptive depth-guided image completion method that addresses the image completion problem using information contained in the rest of the image. The completion process is separated into structure and texture synthesis. A method is first introduced for completing the respective depth map through the use of a diffusion-based operation, preserving global image structure within the unknown region. Building upon the state of the art exemplar based inpainting technique of Barnes et al., we complete the target (unknown) region by matching to and blending source patches drawn from the rest of the image, using the reconstructed depth information to guide the completion process. Secondly, for each target patch, we formulate an adaptive patch size determination as an optimization problem that minimizes an objective function involving local image gradient magnitude and orientations. An extension to the coherence- based objective function introduced by Wexler et al. is then introduced, which not only encourages coherence of the respective target region with respect to the source region in colour but also in depth. We further consider the variance between patches in the SSD criteria for preventing error accumulation and propagation. Experimental results show that our method can provide a significant improvement to patch-based image completion algorithms shown by PSNR and SSIM calculations as well as a qualitative subjective study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".