Low-illumination environment vehicle detection based on optimized adaptive image enhancement

2021 ◽  
Author(s):  
Kun Zhao ◽  
Jian Liu ◽  
Xiaocheng Xiao
Keyword(s):  
Image Enhancement ◽  
Vehicle Detection ◽  
Low Illumination

Low-illumination Image Enhancement for Night-time UAV Pedestrian Detection

2020 ◽  
pp. 1-1
Author(s):  
Weijiang Wang ◽  
Yeping Peng ◽  
Guangzhong Cao ◽  
Xiaoqin Guo ◽  
Ngaiming Kwok
Keyword(s):  
Image Enhancement ◽  
Pedestrian Detection ◽  
Night Time ◽  
Low Illumination

Image Enhancement

2020 ◽  
pp. 211-223
Author(s):  
Rasmita Lenka ◽  
Asimananda Khandual ◽  
Koustav Dutta ◽  
Soumya Ranjan Nayak
Keyword(s):  
Image Enhancement ◽  
Color Constancy ◽  
Correction Method ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Post Processing ◽  
Dark Channel ◽  
Novel Method ◽  
Low Illumination ◽  
Human Eyes

This chapter describes a novel method to enhance degraded nighttime images by dehazing and color correction method. In the first part of this chapter, the authors focus on filtering process for low illumination images. Secondly, they propose an efficient dehazing model for removing haziness Thirdly, a color correction method proposed for color consistency approach. Removing nighttime haze technique is an important and necessary procedure to avoid ill-condition visibility of human eyes. Scattering and color distortion are two major problems of distortion in case of hazy image. To increase the visibility of the scene, the authors compute the preprocessing using WLS filter. Then the airlight component for the non-uniform illumination presents in nighttime scenes is improved by using a modified well-known dark-channel prior algorithm for removing nighttime haze, and then it uses α-automatic color equalization as post-processing for color correction over the entire image for getting a better enhanced output image free from haze with improved color constancy.

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