Xun-Peng Yi

IterRF: Iterative and Interpretable Mutual Enhancement for Joint Multi-Modal Image Registration and Fusion

SeeRF: Learning to See Beyond Fields of View via Generative Image Registration and Fusion

A generative registration-fusion framework that overcomes the limitations imposed by shared fields of view and enables effective completion and natural fusion of non-overlapping field-of-view regions is proposed.

Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning

Diff-RF explores the intrinsic coupling between registration-fusion and information restoration in the degradation conditions, enabling high-quality fusion of unregistered images under complex degradation conditions.

GText-IF: Leveraging Text-Driven Semantics for Degradation-Aware Image Fusion.

An innovative degradation-aware image fusion framework via leveraging text-driven semantics, termed GText-IF, which can perform effective degradation-aware fusion in real degraded image scenarios, exhibiting remarkable advancement, robustness, and practicality.