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most citedDriving-Video Dehazing with Non-Aligned Regularization for Safety Assistance

1 citations · 1 across the 5 of their papers we have counts for

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cs.CV2026

Towards UAV Image Dehazing: A UAV Atmospheric Scattering Model, Benchmark, and Geometry-Aware Deep Unfolding Network

Wenxuan Fang, Jiangwei Weng, Yu Zheng +5

In UAV applications, haze significantly obscures distant details and weaken structural information, hindering the recovery of details. Current UAV scenarios still face two key chal…

cs.CV2025

Learning Inverse Laplacian Pyramid for Progressive Depth Completion

Kun Wang, Zhiqiang Yan, Junkai Fan +2

Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches…

cs.CV2024

Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video

Junkai Fan, Kun Wang, Zhiqiang Yan +4

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enha…

cs.CV2024

DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

Kun Wang, Zhiqiang Yan, Junkai Fan +4

In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial…

cs.CV2024★ 1 cited

Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance

Junkai Fan, Jiangwei Weng, Kun Wang +4

Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especia…