Proceedings ArticleDOI
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Gaofeng Meng,Ying Wang,Jiangyong Duan,Shiming Xiang,Chunhong Pan +4 more
- pp 617-624
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TLDR
An efficient regularization method to remove hazes from a single input image and can restore a high-quality haze-free image with faithful colors and fine image details is proposed.Abstract:
Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.read more
Citations
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Proceedings ArticleDOI
Object Recognition based Self-Position Estimation for Underwater Robots
TL;DR: In this paper , an underwater robot for quay wall inspections is required to observe a specific object while moving parallel to the wall, which contributes to robot self-localization by revealing the robot motion parallel to a wall based on object detection results and depth information from an assistant acoustic system.
Proceedings ArticleDOI
The Impact of Haze Non-Homogeneity on the Recent Image Dehazing Methods
TL;DR: In this paper, the impact of haze non-homogeneity on image dehazing techniques is analyzed using the HH-HAZE21 dataset, which contains 35 nonhomogeneous hazy images and their corresponding haze-free images captured from the same scene.
Book ChapterDOI
An Effective and Efficient Dehazing Method of Single Input Image
TL;DR: An effective and efficient dehazing method is proposed for a single input image by combining the dark channel prior information and a low-light image enhancement model.
Book ChapterDOI
Image Dehazing Based on Deep Multiscale Fusion Network and Continuous Memory Mechanism
Proceedings ArticleDOI
Proper Guidance Image Generation Based on Saliency Factor for Better Transmission Refinement in Image Dehazing
Libao Zhang,Xiaohan Wang +1 more
TL;DR: Saliency detection, which simulates the way human eyes work, is introduced into haze removal to tackle the above issue and has great superiority in detail recovery compared with other state-of-art methods.
References
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Proceedings ArticleDOI
Visibility in bad weather from a single image
TL;DR: A cost function in the framework of Markov random fields is developed, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation, and is applicable for both color and gray images.
Journal ArticleDOI
Single image dehazing
TL;DR: Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
Journal ArticleDOI
Contrast restoration of weather degraded images
TL;DR: A physics-based model is presented that describes the appearances of scenes in uniform bad weather conditions and a fast algorithm to restore scene contrast, which is effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols.
Journal ArticleDOI
Edge-preserving decompositions for multi-scale tone and detail manipulation
TL;DR: This paper advocates the use of an alternative edge-preserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction.
Journal ArticleDOI
Vision and the Atmosphere
TL;DR: This work studies the visual manifestations of different weather conditions, and model the chromatic effects of the atmospheric scattering and verify it for fog and haze, and derives several geometric constraints on scene color changes caused by varying atmospheric conditions.