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.
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
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
By Zhenning Shi, Chen Xu, Junhao Zhang, Kefei Zhang, Linjie Liu, Zhedong Zheng, Tao Li
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
By Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng
WildShadowRemover is a framework that adapts a pretrained video diffusion model for robust in-the-wild video shadow removal using LoRA fine-tuning. It augments the frozen VAE decoder with a detail injection module and introduces a shadow‑mask‑guided frequency‑decomposed modulation module to restore high‑frequency textures while suppressing shadow artifacts, with monocular depth priors providing geometry‑aware guidance. The authors also create WildShadow, a large‑scale paired video shadow removal dataset, and show that their method outperforms existing approaches in shadow removal quality, temporal consistency, and generalization across challenging real‑world scenarios.
By Jiamin Xu, Cong Wang, Zheng Dong, Chi Wang, Renshu Gu, Weiwei Xu, Gang Xu