Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for Visual Sensor Network
Bibliographic record
Abstract
Visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine transform (DCT) basis. The relation is that the eigenvalues of the Laplacian with proper boundary condition form the diagonal elements of the diagonal matrix generated by the DCT operation on the Laplacian. Exploiting this relation, we propose a focus measure which works on the DCT coefficients reflecting the spatial-domain Laplacian operation. Certain simplifications allow our focus measure computation through hardware-friendly integer multiplication and summation, where matrix multiplication involves just N scalar multiplications for an N × N 2D signal. Finally, we propose an approach which suitably fuses multi-focus images or video frames in DCT based image or video coding framework through detection of properly focused area and neighborhood consistency analysis. We show that our proposed approach is hardwarefriendly, computationally simple, and is fast enough for VSN. Through experimental results, we show that our approach outperforms the relevant state-of-the-art in multi-focus image fusion for VSN both quantitatively and subjectively. We also show that our approach is effective in comparison to the state-of-the-art and a few latest generic multi-focus image fusion techniques in terms of quantitative and subjective evaluations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".