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:
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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Posted Content
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
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Journal ArticleDOI
Region-Based Dehazing via Dual-Supervised Triple-Convolutional Network
TL;DR: Zhang et al. as discussed by the authors proposed a region-based dehazing method via dual-supervised triple-convolutional network (TCN) to solve the contrast degradation in a dark or shadow region.
Journal ArticleDOI
A Review of Remote Sensing Image Dehazing.
TL;DR: In this article, the authors classified the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven, and summarized the advantages and disadvantages of each type of algorithm in detail.
Proceedings ArticleDOI
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
Sohyun Lee,Tae-Il Son,Suha Kwak +2 more
TL;DR: A new method for learning semantic segmentation models robust against fog is proposed, which substantially outperforms previous work on three real foggy image datasets and improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes.
Proceedings ArticleDOI
Reinforced Depth-Aware Deep Learning for Single Image Dehazing
Tiantong Guo,Vishal Monga +1 more
TL;DR: DDRL generates the dehazed image in a near-to-far progressive manner by utilizing the depth-information from the scene, which contrasts with the most recent learning-based methods that estimate these parameters in one pass.
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.