← Back to all news
arXiv Computer Vision By Paula Garrido-Mellado, Daniel Feijoo, Yuning Cui, Alvaro Garcia, Marcos V. Conde

Efficient All-in-One Weather Restoration using Spectral Harmonization

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Sep 10

DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing

DPSF-Net is a dual‑prior spatial‑frequency network designed for real‑world remote sensing image dehazing. It combines hazy RGB images with dark channel prior maps as joint inputs, and incorporates a spatial‑frequency residual interaction block, a prior‑guided feature attention module, and a selective kernel complementary fusion module to reduce colour shift, structural distortion, and large‑scale haze. Experiments show that DPSF-Net achieves state‑of‑the‑art performance on the RRSHID benchmark while maintaining a favorable balance of restoration quality, parameter count, and computational complexity.

By Mei Lu, Shangliang Shao, Shanliang Yao
benchmarks
More like this →
arXiv Computer Vision
5d ago

WaFIR: Wavelet-Fourier Mixture-of-Experts with Subband-Adaptive Routing for Blind All-in-One Image Restoration

arXiv:2610.29108v1 Announce Type: new Abstract: Blind all-in-one image restoration must adapt a shared model to heterogeneous and sometimes mixed degradations without a task label at test time. We in...

By Trong Nhan Nguyen, Vu Hoang Minh Doan, Thanh Dat Le, Truong Tien Vo, Thi Thu Ha Vu, Tan Hung Vo, Jaeyeop Choi, Junghwan Oh, Truong-Son Hy
More like this →
arXiv AI
Sep 24

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.

By Xinyao Wang, Lijun He, Zhihan Ren, Fan Li
agentsrobotics
More like this →