Tri‑DehazeGS is a Gaussian Splatting framework that decouples clean scene reconstruction from atmospheric haze by representing the scene with Gaussian primitives and the haze medium with an independent view‑shared tri‑plane field. It uses a physical scattering model to compose hazy observations and introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to re‑balance gradients in low‑transmittance regions without altering forward rendering. Experiments on real and synthetic haze benchmarks demonstrate that this approach improves clean novel‑view reconstruction.
By Kui Jiang, Yang Gu, Jiacheng Liu, Shiyu Liu, Youyu Chen, Hui Liu
Tri-DehazeGS tackles the problem of reconstructing clean 3D scenes from hazy multi‑view images by decoupling the scene and the haze medium. It represents the scene with Gaussian primitives while modeling the haze as an independent view‑shared tri‑plane field, and composes hazy observations through a physical scattering model. The method introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to balance gradients in low‑transmittance regions, leading to improved novel‑view reconstruction on real and synthetic haze benchmarks.
arXiv:2607. 05467v1 Announce Type: cross Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream detection and tracking.
By Amir Pouladi, Vesal Ahsani, Haijun Li, Homayoun Najjaran, Afzal Suleman
arXiv:2610.00141v1 Announce Type: new
Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...
By Gur Levy Birkental, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
Existing diffusion-based methods have recently made significant progress in image dehazing. However, they typically neglect the physics of haze formation and reconstruct clean images from pure Gaussian noise, thereby limiting their restoration potential.
arXiv:2609.37003v1 Announce Type: new
Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most e...
By Danfeng Hong, Chenyu Li, Jocelyn Chanussot
arXiv:2608.22289v1 Announce Type: new
Abstract: Unmanned aerial vehicles (UAVs) increasingly require robust visual localization in GNSS-denied environments. A common solution estimates UAV poses by m...
By Yibin Ye, Xichao Teng, Shuo Chen, Xiaokai Song, Dongdong Guan, Qifeng Yu, Zhang Li
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
The paper introduces 3D-USE, a two‑stage framework for underwater scene‑level enhancement that learns a persistent, visibility‑enhanced 3D representation from degraded multi‑view observations. First, the Medium Radial Basis Anchor Representation (MediumRBF) builds a medium‑aware Gaussian scene by separating object and medium effects. Then, Appearance Transition Consensus (ATC) transfers 2D underwater image enhancement knowledge into scene‑global and Gaussian‑local targets, which are realized by an Underwater Bilateral Appearance Field (U‑BAF) to render enhanced novel views without a 2D UIE model at inference. Experiments on real underwater scenes demonstrate improved visibility, cross‑view consistency, and preserved reconstruction quality.
By Jieyu Yuan, Yuanlin Zhang, Jihong Li, Chunle Guo, Huimin Lu, Chongyi Li
arXiv:2608.23136v1 Announce Type: new
Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspect...
By Hu Wang, Hongxu Pu, Zhiqi Hu, Fangzhou Lin, Wang Wang
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general o...
Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspection. This paper investigates whether degradation-...