The paper introduces UAR-Net, a weather‑specific all‑in‑one restoration framework that uses a gated transformer and balanced multi‑scale skip connections to handle spatially heterogeneous weather degradations. It incorporates Gated Dual‑scale Transformer Blocks for selective global and local modeling, a Progressive Balanced Multi‑scale Skip Connection for feature integration, and an Uncertainty‑Aware Refinement Head that removes artifacts, enhances details, and estimates predictive uncertainty. The network is trained with a Brightness‑Aware Energy Loss to promote accurate reconstruction and well‑calibrated uncertainty, achieving state‑of‑the‑art results on multiple adverse‑weather benchmarks.
By Zheke Jin, Yuning Cui, Tianle Jin, Alois Knoll, Hu Cao
The paper introduces TSGPD-IR, a network that disentangles weather-induced artifacts from true thermal signals in infrared images. It uses weather semantics and regional degradation severity to generate adaptive prompts, estimate severity without manual labels, and select appropriate expert modules for restoration. This approach aims to reduce artifacts and preserve weak thermal details across varying weather conditions.
By Xinyao Wang, Lijun He, Zhihan Ren, Fan Li
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors.
The paper introduces PGL-Net, a lightweight physics-inspired network for real‑world single image dehazing. It decouples the task into global distribution rectification and local structural refinement, using a Physics‑Inspired Affine Fusion module for global alignment and a Degradation‑Aware Modulation block for adaptive detail restoration. Experiments show that PGL‑Net, especially its Tiny variant, outperforms recent state‑of‑the‑art methods in PSNR and downstream object detection while reducing inference latency by over tenfold.
By Yifei Qu, Ru Li, Junjie Chen, Jinyuan Wu
arXiv:2609.16578v1 Announce Type: new
Abstract: The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing me...
By Hu Gao, Lizhuang Ma, Yulong Chen
arXiv:2605. 13258v2 Announce Type: replace-cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions.
By Youwei Pan, Leilei Cao, Yingfang Zhu, Fengjie Zhu