Single image dehazing via multi‐scale gradient domain contrast enhancement

Mi, Zetian; Zhou, Huan; Zheng, Yijun; Wang, Minghui

doi:10.1049/iet-ipr.2015.0112

Cited by 37 publications

(13 citation statements)

References 19 publications

(70 reference statements)

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“…In computer vision and computer graphics, the traditional physical atmospheric scattering model or also known as the dichromatic model [2] has been widely used to describe the formation of hazy image and to dehazing [9]- [11], [13]- [22]. It can be defined in Eq.…”

Section: Problem Statement a Hazy Image In Computer Visionmentioning

confidence: 99%

“…Recently, researchers used various techniques for single image-based haze removal. For example, contrast enhancement algorithms were proposed by [7], [8], and [9], Multi-scale fusion algorithms were proposed by [10] and [11], Retinex dehazing algorithm was proposed by [12], and the most popular conventional single haze removal algorithm is based on physics-based algorithm or dichromatic model [2], [13]- [19].…”

Section: Introductionmentioning

confidence: 99%

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Single Image Haze Removal Using Deep Cellular Automata Learning

Tangsakul

Wongthanavasu

2020

IEEE Access

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Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA's learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multilayer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.

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Section: Problem Statement a Hazy Image In Computer Visionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single Image Haze Removal Using Deep Cellular Automata Learning

Tangsakul

Wongthanavasu

2020

IEEE Access

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“…As this method does not depend on the physical atmospheric particles imaging model, its colour compensation may fail when colour cast appears in the original atmospheric particulate image. The transmission of the sky region is underestimated, so the colour of this region is overcompensated and colour cast is more obvious [23], [24].…”

Section: Fig 4 Single Image Approach Classmentioning

confidence: 99%

A Study of Atmospheric Particles Removal in a Low Visibility Outdoor Single Image

Talib¹,

Nasirudin²,

Abdullah³

2016

International Journal on Advanced Science, Engineering and Information Technology

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Maximum limit of human visibility without the assistance of equipment is 1000 m based on International Commission on Illumination. The use of a camera in the outdoor for the purpose of navigation, monitoring, remote sensing and robotic movement sometimes may yield images that are interrupted by haze, fog, smoke, steam and water drops. Fog is the random movement of water drops in the air that normally exists in the early morning. This disorder causes a differential image observed experiences low contrast, obscure, and difficult to identify targets. Analysis of the interference image can restore damaged image as a result of obstacles from atmospheric particles or drops of water during image observation. Generally, images with atmospheric particles contain a homogeneous texture like brightness and a heterogeneous texture which is the object that exists in the atmosphere. A pre-processing method based on the dark channel prior statistical measure of contrast vision and prior knowledge still produces good image quality but less effective to overcome Halo problem or ring light, and strong lighting. This study aims to propel the development of machine vision industry aimed at navigation or monitoring for ground transportation, air or sea.

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“…Currently, these existing approaches for haze removal can be grouped into the contrast-based methods [1]- [3], the fusion-based methods [4], [5] and the physicallybased methods [6]- [22].…”

Section: Introductionmentioning

confidence: 99%

Single Image Dehazing Using Invariance Principle

Zhang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose a novel technique to increase the visibility of the hazy image. Benefiting from the atmospheric scattering model and the invariance principle for scene structure, we formulate structure constraint equations that derive from two simulated inputs by performing gamma correction on the input image. Relying on the inherent boundary constraint of the scattering function, the expected scene albedo can be well restored via these constraint equations. Extensive experimental results verify the power of the proposed dehazing technique. key words: image dehazing, inhomogeneous atmosphere, atmospheric scattering model, depth map

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Single image dehazing via multi‐scale gradient domain contrast enhancement

Cited by 37 publications

References 19 publications

Single Image Haze Removal Using Deep Cellular Automata Learning

Single Image Haze Removal Using Deep Cellular Automata Learning

A Study of Atmospheric Particles Removal in a Low Visibility Outdoor Single Image

Single Image Dehazing Using Invariance Principle

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