Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914583108648960 |
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| author | Harada, Shimpei Hara, Junya Higashi, Hiroshi Tanaka, Yuichi |
| author_facet | Harada, Shimpei Hara, Junya Higashi, Hiroshi Tanaka, Yuichi |
| contents | This paper proposes a joint alignment and denoising method for event-based vision sensors (EVSs). Existing signal processing methods for EVSs typically perform event alignment (EA) and event denoising (ED) as separate modules. However, this separation creates a dilemma: without ED, EA is biased by noise, whereas without EA, ED struggles to distinguish signal events from noise ones. To address this dilemma, we jointly optimize EA and ED by formulating a bi-objective Pareto optimization problem. Our formulation is built upon a contrast map that counts the number of events localized in each pixel. With a contrast map, we can formulate EA as maximizing its variance and ED as minimizing the variance. We cast these two conflicting problems as a Pareto optimization and use a regret strategy to obtain a solution. Experimental results on denoising and motion estimation demonstrate that our method achieves improvements against alternative ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21096 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization Harada, Shimpei Hara, Junya Higashi, Hiroshi Tanaka, Yuichi Image and Video Processing This paper proposes a joint alignment and denoising method for event-based vision sensors (EVSs). Existing signal processing methods for EVSs typically perform event alignment (EA) and event denoising (ED) as separate modules. However, this separation creates a dilemma: without ED, EA is biased by noise, whereas without EA, ED struggles to distinguish signal events from noise ones. To address this dilemma, we jointly optimize EA and ED by formulating a bi-objective Pareto optimization problem. Our formulation is built upon a contrast map that counts the number of events localized in each pixel. With a contrast map, we can formulate EA as maximizing its variance and ED as minimizing the variance. We cast these two conflicting problems as a Pareto optimization and use a regret strategy to obtain a solution. Experimental results on denoising and motion estimation demonstrate that our method achieves improvements against alternative ones. |
| title | Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2605.21096 |