Optimization of Low Illumination Image Enhancement Algorithm Based on Dark Channel Prior

Author(s):  
Jifei Feng ◽  
Qian Chen ◽  
Weiji He ◽  
Guohua Gu ◽  
Wenwen Zhang ◽  
...  
2021 ◽  
Vol 2083 (4) ◽  
pp. 042008
Author(s):  
Zhe Wu ◽  
Jianfgui Han ◽  
Chenghao Cao

Abstract All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.


Author(s):  
Rasmita Lenka ◽  
Asimananda Khandual ◽  
Koustav Dutta ◽  
Soumya Ranjan Nayak

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.


2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740044 ◽  
Author(s):  
Lintao Zheng ◽  
Hengliang Shi ◽  
Ming Gu

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 85
Author(s):  
Lingli Guo ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.


2019 ◽  
Vol 48 (7) ◽  
pp. 710005
Author(s):  
梅英杰 MEI Ying-jie ◽  
宁媛 NING Yuan ◽  
陈进军 CHEN Jin-jun

Sign in / Sign up

Export Citation Format

Share Document