Han Xu

MSFA-Net: A Low-Light Image Enhancement Method Based on Multi-Scale Feature Fusion and Attention Mechanism

Low-light images are degraded by uneven illumination, sensor noise, color distortion, and weak texture boundaries. To address insufficient detail recovery, noise amplification, and inadequate coordination between global illumination and local texture, this paper proposes MSFA-Net, a compact residual enhancement network...

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.