Proceedings ArticleDOI
Non-local Image Dehazing
Dana Berman,Tali Treibitz,Shai Avidan +2 more
- pp 1674-1682
TLDR
This work proposes an algorithm, linear in the size of the image, deterministic and requires no training, that performs well on a wide variety of images and is competitive with other state-of-the-art methods on the single image dehazing problem.Abstract:Â
Haze limits visibility and reduces image contrast in outdoor images. The degradation is different for every pixel and depends on the distance of the scene point from the camera. This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel. Previous methods solve the single image dehazing problem using various patch-based priors. We, on the other hand, propose an algorithm based on a new, non-local prior. The algorithm relies on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors, that form tight clusters in RGB space. Our key observation is that pixels in a given cluster are often non-local, i.e., they are spread over the entire image plane and are located at different distances from the camera. In the presence of haze these varying distances translate to different transmission coefficients. Therefore, each color cluster in the clear image becomes a line in RGB space, that we term a haze-line. Using these haze-lines, our algorithm recovers both the distance map and the haze-free image. The algorithm is linear in the size of the image, deterministic and requires no training. It performs well on a wide variety of images and is competitive with other stateof-the-art methods.read more
Citations
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Patent
Depth calculating method under haze environment
TL;DR: In this article, the authors proposed a depth estimation method under a haze environment, which consists of capturing the color map of a current scene with a hand-held camera or an image acquisition device, and then using it to estimate the PM2.5 value of the current scene.
Book ChapterDOI
VLW-Net: A Very Light-Weight Convolutional Neural Network (CNN) for Single Image Dehazing.
TL;DR: A very light-weight end-to-end CNN network (VLW-Net) for single image haze removal and a new Inception structure is proposed by combining it with a reformulated atmospheric scattering model, which is at least 6 times more light- Weight than the state-of-the-arts.
DissertationDOI
Alternating Optimization: Constrained Problems, Adversarial Networks, and Robust Models
TL;DR: This dissertation focuses on machine learning problems that can be formulated as a minimax problem in training, and study alternating optimization methods served as fast, scalable, stable and automated solvers, including adaptive ADMM (AADMM), which is a fully automated solver achieving fast practical convergence by adapting the only free parameter in ADMM.
Journal ArticleDOI
MFFE: Multi-scale Feature Fusion Enhanced Net for image dehazing
Xin Zhang,Jinjiang Li,Zhen Hua +2 more
TL;DR: Zhang et al. as discussed by the authors designed a multiscale feature fusion module that can effectively compensate for the missing contextual information and make full use of the disjoint features, and an improved local binary pattern and SE attention mechanism are used to help the network obtain clearer details and texture images.
Journal ArticleDOI
Unsupervised single image dehazing with generative adversarial network
Wei-Ya Ren,Li Zhou,Jie Chen +2 more
TL;DR: In this paper , an end-to-end network based on GAN architecture is established and fed with unpaired clean and hazy images, signifying that the estimation of atmospheric light and transmission is not required.
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.
Proceedings ArticleDOI
Fast visibility restoration from a single color or gray level image
TL;DR: A novel algorithm and variants for visibility restoration from a single image which allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera.
Proceedings ArticleDOI
Single image haze removal using dark channel prior
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.
Journal ArticleDOI
Dehazing Using Color-Lines
TL;DR: A new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines is described.