arXiv Computer Vision October 19, 2026 By Rui Liu, Lin Gu, Hesong Li, Ying Fu

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

Read the original on arXiv Computer Vision →

PCDFusion is a new infrared-visible image fusion method that treats fusion as a local Bayesian posterior rendering problem. It uses a visible-anchored framework with three rendering stages to preserve useful thermal information, correct exposure-degraded visible areas, and incorporate reliable infrared structures into the final image. Experiments on public benchmarks demonstrate improved fusion quality and better performance on downstream detection and segmentation tasks under challenging lighting conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computer Vision

Sep 17

EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion

arXiv:2603.16130v5 Announce Type: replace Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in vis...

By Zhiwei Wang, Defeng He, Li Zhao, Xiaoqin Zhang, Yuxing Li, Edmund Y. Lam

multimodal

Hugging Face Trending Papers

Aug 13

P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation

Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.

computer-vision efficiency multimodal benchmarks

arXiv AI

Jul 21

Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation

arXiv:2607. 17611v1 Announce Type: cross Abstract: Multi-exposure fusion (MEF) expands the luminance range beyond what a single exposure can capture.

By Sangmin Han, Jinho Kim, Jinwoo Kim, Dongyoung Kim, Seon Joo Kim

diffusion

Hugging Face Trending Papers

Jul 27

BeyondFusion: Self-Aligned Latent Diffusion for Calibration-Free Infrared Super-Resolution and Infrared-Visible Fusion

Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cross-sensor misalignment caused by different optics, viewpoints, fields of view, and exposure timings hinders practical deployment.

diffusion multimodal benchmarks

arXiv Computer Vision

Oct 16

VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions

VesperMUSE is a multi‑modal vision transformer that enhances image semantic segmentation under varied lighting by combining physics‑informed low‑light adaptation, thermal‑guided low‑light image enhancement, and surface normal maps. The framework fuses RGB, thermal, and normal features through a modality‑agnostic self‑attention mechanism, enabling robust segmentation without architectural changes for different sensor setups. Extensive tests show that VesperMUSE recovers missing visual cues and achieves state‑of‑the‑art performance in extreme, unstructured environments.

By Raul Balmez

llms agents computer-vision multimodal benchmarks

arXiv Machine Learning

Aug 11

FusionRelight: Relighting Portraits in Real Time via Hybrid Domain Knowledge Fusion

arXiv:2604. 23094v2 Announce Type: replace-cross Abstract: Portrait relighting is a low-level vision problem in which physically plausible illumination transfer, identity preservation, and compact real-time inference must be considered together.

By Qian Huang, Mayoore Selvarasa Jaiswal, Zhen Zhong, Rochelle Pereira, Jianyuan Min

diffusion benchmarks