7 papers
M2IR: Proactive All-in-One Image Restoration via Mamba-style Modulation and Mixture-of-Experts
Shiwei Wang, Yongzhen Wang, Bingwen Hu +3
While Transformer-based architectures have dominated recent advances in all-in-one image restoration, they remain fundamentally reactive: propagating degradations rather than proac…
AlignFreeNet: Is Cross-Modal Pre-Alignment Necessary? An End-to-End Alignment-Free Lightweight Network for Visible-Infrared Object Detection
Dingkun Zhu, Haote Zhang, Lipeng Gu +7
Cross-modal misalignments, such as spatial offsets, resolution discrepancies, and semantic deficiencies, frequently occur in visible-infrared object detection (VI-OD). To mitigate…
Laplace-Mamba: Laplace Frequency Prior-Guided Mamba-CNN Fusion Network for Image Dehazing
Yongzhen Wang, Liangliang Chen, Bingwen Hu +3
Recent progress in image restoration has underscored Spatial State Models (SSMs) as powerful tools for modeling long-range dependencies, owing to their appealing linear complexity…
M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration
Yongzhen Wang, Yongjun Li, Zhuoran Zheng +2
Natural images are often degraded by complex, composite degradations such as rain, snow, and haze, which adversely impact downstream vision applications. While existing image resto…
WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
Jie Sun, Heng Liu, Yongzhen Wang +2
In this paper, we reveal a novel haze-specific wavelet degradation prior observed through wavelet transform analysis, which shows that haze-related information predominantly reside…
DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration
Jiamei Xiong, Xuefeng Yan, Yongzhen Wang +3
Image restoration under adverse weather conditions is a critical task for many vision-based applications. Recent all-in-one frameworks that handle multiple weather degradations wit…