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Jin-Yuan Liu

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Book Open access Nov 2026

Learning Robust 3D Gaussian Representations via Discrepancy-Aware Infrared and Visible Modality Fusion

Infrared and visible image fusion (IVIF) combines robust infrared sensing with the rich structural details of visible imagery. Existing IVIF methods are predominantly developed in 2D image space, limiting them to fixed viewpoints and hindering explicit modeling of modality discrepancies at the scene level. Although rec...

Guan-Yao Wu, Hao-Ze Song, Zengxi Zhang et al. · 0 citations
Open access 2026

Prior-Guided One-Step Diffusion Model for Multi-Exposure Image Fusion

Fusing multi-exposure images into a single high-quality image requires preserving structural details and faithful colors while maintaining efficient inference. Although diffusion-based methods provide strong natural image priors, their iterative denoising process usually requires dozens to hundreds of steps, leading to...

Xiao-Shan Liu, Heng Zhang, Yan Peng et al. · 0 citations
Oct 2026

Leveraging Semantic Priors for Robust Multi-Exposure Image Registration and Fusion.

Multi-exposure image fusion (MEF) aims to combine a sequence of differently exposed images into a single high-quality photograph. However, spatial misalignment caused by camera shake or object motion often leads to severe ghosting artifacts in the fused image. Existing methods struggle with large displacements and comp...

Jin-Yuan Liu, Qian-Jun Huang, Di Wang et al. · 0 citations
Aug 2026

Symbiotic Evolutionary Learning for Task-Adaptive Infrared and Visible Image Fusion.

This paper proposes a symbiotic evolutionary learning framework for task-adaptive IVIF, termed EvoFuse, which enables a mutual adaptation process where the fusion network and task models are jointly updated under a Pareto-inspired non-degradation criterion.

Jin-Yuan Liu, Bowei Zhang, Ludan Sun et al. · 1 citation
Aug 2026

Universal Representation for Real-World Misaligned Infrared-Visible Image Fusion.

Infrared and visible image fusion is pivotal for robust visual perception across all weather conditions and scenes. Although deep learning-based methods have made notable progress, most either assume pre-aligned inputs or rely on implicit feature-space alignment, which fails to fundamentally address the amplification o...

Jin-Yuan Liu, Zengxi Zhang, Jiahao Zhang et al. · 1 citation

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