Multifocus Image Fusion With Complex Sparse Representation
Bibliographic record
Abstract
Multifocus image fusion aims to merge source images with distinct focused areas into a single, fully focused fused image. Sparse representation (SR) stands out as a robust signal modeling technique that has achieved remarkable success in multifocus image fusion. Numerous SR-based fusion methods have been proposed over the years, underscoring the importance of SR in enhancing fusion quality. However, a fundamental problem appearing in most existing SR models is the absence of directionality. This deficiency restricts their capacity to extract intricate details. To address this issue, we propose the complex SR ($\mathbb {C}$SR) model for image fusion. This model utilizes the properties of hypercomplex signals to extract directional information from real-valued signals through complex extension. Subsequently, the directional components of the input signal are decomposed into sparse coefficients over corresponding directional dictionaries. The key advantage of our design over conventional SR models is the ability to capture the geometrical image structures effectively, since$\mathbb {C}$SR coefficients can provide precise measurements of detailed information along specific directions. Experimental results conducted on three widely used multifocus image fusion datasets substantiate the superiority of our method over 17 representative multifocus image fusion methods in terms of both visual quality and objective evaluation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".