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cs.CV2026

UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables

Yibing Zhang, Xunpeng Yi, Qinglong Yan +3

With the advancement of imaging technology, ultra-high-definition images have become increasingly essential in modern visual applications. However, existing multi-focus image fusio…

cs.CV2026

Low Light Image Enhancement Challenge at NTIRE 2026

George Ciubotariu, Sharif S M A, Abdur Rehman +90

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…

cs.CV2025

NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102

This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…

cs.CV2025

LUT-Fuse: Towards Extremely Fast Infrared and Visible Image Fusion via Distillation to Learnable Look-Up Tables

Xunpeng Yi, Yibing Zhang, Xinyu Xiang +3

Current advanced research on infrared and visible image fusion primarily focuses on improving fusion performance, often neglecting the applicability on real-time fusion devices. In…

cs.CV2025

TemCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration

Meiqi Gong, Hao Zhang, Xunpeng Yi +2

Existing multi-modal fusion methods typically apply static frame-based image fusion techniques directly to video fusion tasks, neglecting inherent temporal dependencies and leading…

cs.CV2025

VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion

Linfeng Tang, Yeda Wang, Meiqi Gong +7

Compared to images, videos better reflect real-world acquisition and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complem…