scholarly journals Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

Technologies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 101
Author(s):  
Ho Sang Lee
Keyword(s):  
Image Enhancement ◽  
Dark Channel Prior ◽  
Color Channel ◽  
Dark Channel ◽  
Degraded Image ◽  
Weak Points ◽  
Sand Particles ◽  
The Difference ◽  
Color Cast ◽  
Sand Dust

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.

scholarly journals Let You See in Sand Dust Weather: A Method Based on Halo-Reduced Dark Channel Prior Dehazing for Sand-Dust Image Enhancement

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116722-116733 ◽  
Author(s):  
Zhenghao Shi ◽  
Yaning Feng ◽  
Minghua Zhao ◽  
Erhu Zhang ◽  
Lifeng He
Keyword(s):  
Image Enhancement ◽  
Dark Channel Prior ◽  
Dark Channel ◽  
Dust Weather ◽  
Sand Dust

scholarly journals Specular Reflection Image Enhancement Based on a Dark Channel Prior

2021 ◽  
Vol 13 (1) ◽  
pp. 1-11
Author(s):  
Ye Xin ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov
Keyword(s):  
Image Enhancement ◽  
Specular Reflection ◽  
Dark Channel Prior ◽  
Dark Channel ◽  
Reflection Image

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.

scholarly journals Multi-scale fusion for underwater image enhancement using multi-layer perceptron

2021 ◽  
Vol 10 (2) ◽  
pp. 389
Author(s):  
M. Sudhakara ◽  
M. Janaki Meena
Keyword(s):  
Image Enhancement ◽  
Light Attenuation ◽  
Computational Time ◽  
Multi Layer Perceptron ◽  
Step Method ◽  
Multi Scale ◽  
Underwater Image ◽  
Dark Channel ◽  
Quantitative Results ◽  
The Difference

<span id="docs-internal-guid-54b35aa6-7fff-0992-ed4c-aca4d05cfcfa"><span>Underwater image enhancement (UIE) is an imperative computer vision activity with many applications and different strategies proposed in recent years. Underwater images are firmly low in quality by a mixture of noise, wavelength dependency, and light attenuation. This paper depicts an effective strategy to improve the quality of degraded underwater images. Existing methods for dehazing in the literature considering dark channel prior utilize two separate phases for evaluating the transmission map (i.e., transmission estimation and transmission refinement). Accurate restoration is not possible with these methods and takes more computational time. A proposed three-step method is an imaging approach that does not need particular hardware or underwater conditions. First, we utilize the multi-layer perceptron (MLP) to comprehensively evaluate transmission maps by base channel, followed by contrast enhancement.  Furthermore, a gamma-adjusted version of the MLP recovered image is derived. Finally, the multi-scale fusion method was applied to two attained images. The standardized weight is computed for the two images with three different weights in the fusion process. The quantitative results show that significantly our approach gives the better result with the difference of 0.536, 2.185, and 1.272 for PCQI, UCIQE, and UIQM metrics, respectively, on a single underwater image benchmark dataset. The qualitative results also give better results compared with the state-of-the-art techniques.</span></span>

scholarly journals Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

J ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 15-34
Author(s):  
Ho-Sang Lee
Keyword(s):  
State Of The Art ◽  
Singular Value ◽  
Color Channel ◽  
Low Contrast ◽  
Color Distortion ◽  
Dark Channel ◽  
Color Cast ◽  
Patch Image ◽  
Value Decomposition ◽  
Contrast Image

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

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