The paper introduces UAR-Net, a weather‑specific all‑in‑one restoration framework that tackles spatially heterogeneous degradations in adverse weather. It combines Gated Dual‑scale Transformer Blocks for selective global and local modeling, a Balanced Multi‑scale Skip Connection for feature integration, and an Uncertainty‑Aware Refinement Head that removes artifacts, enhances detail, and estimates predictive uncertainty. The network is trained with a Brightness‑Aware Energy Loss and achieves state‑of‑the‑art results on multiple adverse‑weather benchmarks.
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:2608. 20263v1 Announce Type: new Abstract: We propose UHDformer++, a general Transformer-based framework to solve numerous Ultra-High-Definition (UHD) image restoration tasks.
By Cong Wang, Liyan Wang, Jinshan Pan, Wei Wang, Wenqi Ren, Jun Liu, Xiaochun Cao
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