scholarly journals A remote‐sensing image enhancement algorithm based on patch‐wise dark channel prior and histogram equalisation with colour correction

2020 ◽  
Author(s):  
Fayaz Ali Dharejo ◽  
Yuanchun Zhou ◽  
Farah Deeba ◽  
Munsif Ali Jatoi ◽  
Yi Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Remote Sensing Image ◽  
Dark Channel Prior ◽  
Dark Channel ◽  
Enhancement Algorithm

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.

Haze Removal of Single Remote Sensing Image by Combining Dark Channel Prior with Superpixel

2016 ◽  
Vol 2016 (2) ◽  
pp. 1-6 ◽  
Author(s):  
Yanlin Tian ◽  
Chao Xiao ◽  
Xiu Chen ◽  
Daiqin Yang ◽  
Zhenzhong Chen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Dark Channel Prior ◽  
Haze Removal ◽  
Dark Channel

Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740044 ◽  
Author(s):  
Lintao Zheng ◽  
Hengliang Shi ◽  
Ming Gu
Keyword(s):  
Image Enhancement ◽  
Traffic Engineering ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Dark Channel Prior ◽  
Gamma Correction ◽  
Blurred Vision ◽  
Low Contrast ◽  
Dark Channel ◽  
Enhancement Algorithm

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.

scholarly journals AN IMPROVED AERIAL REMOTE SENSING IMAGE DEFOGGING METHOD BASED ON DARK CHANNEL PRIOR INFORMATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1025-1030
Author(s):  
Z. Zhang ◽  
W. Feng ◽  
T. Wang ◽  
Y. Zhang ◽  
L. Ding
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Prior Information ◽  
Remote Sensing Image ◽  
Dark Channel Prior ◽  
Visual Effect ◽  
Image Defogging ◽  
Dark Channel ◽  
Aerial Remote Sensing ◽  
Color Cast

Aerial remote sensing image is widely used due to its high resolution, abundant information and convenient processing. However, its image quality is easily influenced by clouds and fog. In recent years, fog and haze air pollution is becoming more and more serious in the north of China and its influence on aerial remote sensing image quality is especially obvious. Considering the characters that aerial remote image is usually in huge amount of data and seldom covers sky area, this paper proposes an improved aerial remote sensing image defogging method based on dark channel prior information. First, a 2 % linear stretching is applied to eliminate the haze offset effect and provide a better initial value for later defogging processing. Then the dark channel prior image is obtained by calculating the minimum values of r, g, b channels of each pixel directly. Subsequently, according to the particularity of aerial image, the adaptive threshold t0 is set up to improve the defogging effect. Finally, to improve the color cast phenomenon, a way called automatic color method is introduced to enhance the visual effect of defogged image. Experiments are performed on normal image in fog and on aerial remote sensing image in fog. Experimental results prove that the proposed method can obtain the defogged image with better visual effect and image quality. Moreover, the improved method significantly balances the color information in the defogged image and efficiently avoids the color cast phenomenon.

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