Dehazing Using Color-Lines

Fattal, Raanan

doi:10.1145/2651362

Cited by 929 publications

(446 citation statements)

References 19 publications

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“…As can be seen in Fig. 16b , d, except for Kopf [5], Fattal [25], and ours, the other algorithms tend to cause more or less distortion and overenhancement (e.g., the rock region in Fig. 16b).…”

Section: B Imentioning

confidence: 54%

Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.

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Section: B Imentioning

confidence: 54%

Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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“…The term I is the image intensity as an RGB color vector 1 , while x is the 2D image spatial location. The term L ∞ is the atmospheric light that is assumed to be globally constant and independent from location x.…”

Section: A Optical Modelingmentioning

confidence: 99%

“…Obtaining quantitative results is challenging as it is difficult to capture ground truth examples for where the same scene has been imaged with and without scattering particles. The work by Fattal [1] synthesized a dataset of by using natural images which associate depth maps that can be used to simulate the spatially varying attenuating in haze and fog images. We have generated an additional dataset using a physically-based rendering to simulate environments with scattered particles.…”

Section: Introductionmentioning

confidence: 99%

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Haze visibility enhancement: A Survey and quantitative benchmarking

Liu

You

Brown

et al. 2017

Computer Vision and Image Understanding

149

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Abstract-This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal [1] and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.

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“…The pixels with the same diffuse chromaticity (x) make (5) a three-dimensional line, and (5) is a lines set for an image with various diffuse chromaticities. In recent literature, the authors of [26] and [27] also use color lines for haze removal. However, their color lines model the medium transmission that describes proportion between ambient light and scene radiance, while our color lines model the surface reflection properties of an object, namely the relation between illumination chromaticity and diffuse chromaticity.…”

Section: Global Color-lines Constraint a Global Color-lines Conmentioning

confidence: 99%

Specular Reflection Separation With Color-Lines Constraint

Ren

Tian

Tang

2017

IEEE Trans. on Image Process.

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Abstract-According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often cannot completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection. Our key observation is from that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely, the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflect the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixelwise way for a single image, and it is suitable for realtime applications. Our experimental results on synthetic and real images show that our method performs better than the state-ofthe-art methods to separate specular reflection.

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Dehazing Using Color-Lines

Cited by 929 publications

References 19 publications

Single image dehazing via an improved atmospheric scattering model

Single image dehazing via an improved atmospheric scattering model

Haze visibility enhancement: A Survey and quantitative benchmarking

Specular Reflection Separation With Color-Lines Constraint

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