Low-Complexity 4-D IIR Filters for Multi-Depth Filtering and Occlusion Suppression in Light Fields
Bibliographic record
Abstract
Light field signal processing allows manipulation of a rich set of information captured from a scene to achieve real-time depth filtering and occlusion suppression. Low-complexity four-dimensional infinite impulse response digital filters for simultaneous depth filtering and occlusion suppression over multiple depths in light fields are proposed. A low-complexity two-dimensional separable approach is employed to design the proposed filters having multiple frequency-planar pass-bands/stopbands in the four-dimensional spatial frequency domain, that can be electronically tuned to enhance/reject planar objects at multiple depths in a light field. Filter synthesis details are provided with specific design examples corresponding to 2-passband and 1-stopband cases. Numerically generated and Lytro camera captured light fields are used to verify the effectiveness of the proposed multi-depth-pass and multi-depth-reject filters. For synthetic light field inputs these filters confirm an average denoising, depth filtering and occlusion suppression performance of 20 dB, 20 dB, and 30 dB, respectively.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".