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PCDFusion: Proposal-Context-Detail Bayesian Rendering for Infrared-Visible Image Fusion

Paper recorded by Signals 4 on 2026-10-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.CV · 计算机视觉 · first seen 2026-10-19

Abstract

Infrared-visible image fusion (IVIF) aims to combine visible and thermal infrared observations for human perception and downstream vision tasks. Visible and thermal infrared observations are physically complementary but unreliable in different ways. Existing deep IVIF methods mainly improve feature interaction and reconstruction, but often lack an explicit local rule for deciding when visible appe

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#242 most recent of 756 cs.CV papers we have recorded · ↑ newer: Agentic Physics-Grounded Video Generation via Memory-Augmented In-Cont · ↓ older: An Efficient Approach to Beetle Damage Detection of Banana Using Deep
Cite this page: PCDFusion: Proposal-Context-Detail Bayesian Rendering for Infrared-Visible Image Fusion: the #242 most recent of 756 cs.CV papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/pcdfusion-proposal-context-detail-bayesian-rendering-for-infrared-visible-image-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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