scholarly journals Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 85
Author(s):  
Lingli Guo ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov
Keyword(s):  
Dark Channel Prior ◽  
Video Enhancement ◽  
Surveillance Video ◽  
Image Brightness ◽  
Practical Applications ◽  
Video Frames ◽  
Refinement Method ◽  
Dark Channel ◽  
Low Illumination ◽  
Enhancement Algorithm

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.

scholarly journals Research on underwater image enhancement algorithm based on improved DCP

2021 ◽  
Vol 2083 (4) ◽  
pp. 042008
Author(s):  
Zhe Wu ◽  
Jianfgui Han ◽  
Chenghao Cao
Keyword(s):  
Image Enhancement ◽  
Transfer Rate ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Underwater Image ◽  
Depth Analysis ◽  
Dark Channel ◽  
Color Bias ◽  
Enhancement Algorithm ◽  
Priority Algorithm

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.

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.

Fog Removal Algorithms for Real-Time Video Footage in Smart Cities for Safe Driving

2019 ◽  
pp. 55-86
Author(s):  
Neetu Sood ◽  
Indu Saini ◽  
Tarannum Awasthi ◽  
Milin Kaur Saini ◽  
Parul Bhoriwal ◽  
...  
Keyword(s):  
Time Complexity ◽  
Smart Cities ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Safe Driving ◽  
Video Footage ◽  
Video Frames ◽  
Dark Channel ◽  
Retinex Algorithm ◽  
Before And After

In this chapter, different approaches are presented for removal of fog from video footage taken in moving cars. The methodology uses different approaches, namely dark channel prior, contrast limited adaptive histogram equalization (CLAHE), the combination of two approaches (dark channel prior and CLAHE), and RETINEX algorithm combined with DWT. The algorithms are implemented in MATLAB R2015a. Moreover, the algorithms are compared based on their computational complexity and a visibility metric which is used for computing the CNR of video frames before and after the application of the algorithm. The chapter discusses which algorithm would provide better performance during night fog and daylight fog. Finally, the safe speed of the driver is calculated based on the time complexity of the algorithm used.

scholarly journals Real Time Night Vision Surveillance using Improved Dark Channel Prior

2021 ◽  
Vol 9 (VI) ◽  
pp. 1581-1590
Author(s):  
M. V. Naga Bhushanam
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Night Vision ◽  
Perceptual Quality ◽  
Dark Channel Prior ◽  
Video Enhancement ◽  
Maximum Information ◽  
Gaussian Pyramid ◽  
Dark Channel

Videos taken under low lighting conditions usually result in severe loss of visibility and contrast and are uncomfortable for observation and analysis. Night vision cameras that cater to the needs are expensive and less versatile. To be cost effective and extract maximum information from videos taken in low lit conditions, video enhancing techniques must be used. Though there are many night vision enhancement techniques available in literature, this paper particularly emphasizes about Improved Dark Channel Prior algorithm and its results. This approach suits well for real time night video enhancement. It has been found that a pixel-wise inversion of a night video appears very similar to the video obtained during foggy days. The same idea of haze removal approach is used to boost the visual quality of night videos. An improved dark channel prior model is presented that is integrated with Gaussian Pyramid operators for local smoothing. The experimental results show that the proposed method can boost the perceptual quality of detailing in night videos.

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