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
Single image dedusting by non-overlap stitching
TL;DR: Experimental results show that this single image dedusting method can effectively remove dust in the image and significantly improve the image quality obviously.
Journal ArticleDOI
Self-Adaptation Feature Attention Network with Multi-Step Fusion for Single Image Dehazing
TL;DR: An efficient end-to-end self-adaptation feature attention (SAFA) network with multi-step fusion that can adaptively expand the receptive field to obtain the key structure information in space and extract more comprehensive and accurate features.
Book ChapterDOI
An Enhanced Depth Approximation Model for Haze Removal Using Single Image
TL;DR: In this paper, a color attenuation prior-based depth approximation model was proposed to approximate depth of a pixel from the camera using a single degraded image, where the depth estimation is directly proportional to the difference of the saturation from the sum of brightness and hue.
Proceedings ArticleDOI
Fuzzy Logic-Refined Color Channel Transfer Synergism based Image Dehazing
TL;DR: A novel Fuzzy logic based reference image generation technique which restricts the intensities of generated reference images within allowable ranges by introducing a control parameter ‘k’ which enables RCCT to serve as an ideal preprocessing step of various daytime, nighttime and underwater dehazing methods.
Book ChapterDOI
Boosting Supervised Dehazing Methods via Bi-level Patch Reweighting
TL;DR: This paper proposed a bi-level dehazing (BILD) framework by designing an internal loop for weighted supervised de-hazing and an external loop for training patch reweighting.
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.