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arXiv Computer Vision

Uncertainty-Guided Adverse Weather Restoration via Gated Transformer Network

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.

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Sep 2

Uncertainty-Guided Adverse Weather Restoration via Gated Transformer Network

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.

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arXiv AI
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Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration

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.

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Hugging Face Trending Papers
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CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration

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.

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Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

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.

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arXiv Computer Vision
Oct 12

MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution

MOSAIC is an object‑centric framework that adapts pretrained diffusion Transformers for image restoration by learning compact structural conditions. It organizes degraded images into structural slots capturing object‑level and layout‑level evidence, integrates these across resolutions, and retrieves recurring priors from a shared prototype memory. Injecting these representations into selected layers of a frozen diffusion Transformer via lightweight residual modulation preserves the pretrained generative prior, yielding improved remote sensing image super‑resolution performance on UCMerced, AID, and RSSCN7 datasets.

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Sep 3

Efficient All-in-One Weather Restoration using Spectral Harmonization

The paper introduces FReSH-IR, a lightweight all-in-one weather restoration method that decomposes feature representations into high- and low-frequency components across a hierarchical encoder-decoder. By integrating spectral decomposition with Fourier-based skip connections, it captures complementary frequency information while preserving spatial detail. The approach achieves comparable restoration quality to transformer-based models while using 80% fewer parameters and operations, making it efficient for high-resolution images and constrained-resource systems.

By Paula Garrido-Mellado, Daniel Feijoo, Yuning Cui, Alvaro Garcia, Marcos V. Conde
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