Citation: Shi, F.; Jia, Z.; Lai, H.; Song,
S.; Wang, J. Sand Dust Images
Enhancement Based on Red and Blue
Channels. Sensors 2022, 22, 1918.
https://doi.org/10.3390/s22051918
Academic Editors: Sławomir
Nowaczyk, Rita P. Ribeiro and
Grzegorz Nalepa
Received: 29 January 2022
Accepted: 27 February 2022
Published: 1 March 2022
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Article
Sand Dust Images Enhancement Based on Red and
Blue Channels
Fei Shi
1,2
, Zhenhong Jia
1,2,
* , Huicheng Lai
1,2
, Sensen Song
1,2
and Junnan Wang
1,2
1
School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China;
sigofei@xju.edu.cn (F.S.); lai@xju.edu.cn (H.L.); song_sen_sen@stu.xju.edu.cn (S.S.);
1254982138@stu.xju.edu.cn (J.W.)
2
Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China
* Correspondence: jzhh@xju.edu.cn; Tel.: +86-0991-8583362
Abstract:
The scattering and absorption of light results in the degradation of image in sandstorm
scenes, it is vulnerable to issues such as color casting, low contrast and lost details, resulting in
poor visual quality. In such circumstances, traditional image restoration methods cannot fully
restore images owing to the persistence of color casting problems and the poor estimation of scene
transmission maps and atmospheric light. To effectively correct color casting and enhance visibility
for such sand dust images, we proposed a sand dust image enhancement algorithm using the red and
blue channels, which consists of two modules: the red channel-based correction function (RCC) and
blue channel-based dust particle removal (BDPR), the RCC module is used to correct color casting
errors, and the BDPR module removes sand dust particles. After the dust image is processed by these
two modules, a clear and visible image can be produced. The experimental results were analyzed
qualitatively and quantitatively, and the results show that this method can significantly improve the
image quality under sandstorm weather and outperform the state-of-the-art restoration algorithms.
Keywords: image enhancement; RCC; sand dust images; red channel; BDPR
1. Introduction
Images or videos captured in sandstorm scenes usually have low contrast, poor visi-
bility and yellowish tones. This is because sand dust particles scatter and absorb specific
spectra of light between the imaging devices and the observed objects. Therefore, these
degraded sand dust images will greatly lose their quality and degrade the performance
of computer vision application systems that typically work outdoors during inclement
weather conditions. Such systems include video surveillance systems for public security
monitoring [
1
,
2
], intelligent transportation systems for license plate recognition [
3
,
4
], visual
recognition systems for automatic driving [
5
], and so on. Hence, developing an effective
sand dust image restoration method to restore color and contrast for computer vision
application systems is desirable. To improve the performance of computer vision systems
and restore the visibility of degraded images, some restoration algorithms for degraded
sand dust images have been proposed. Huang [
6
] presented a transformation method
that enhances the contrast of degraded images via the gamma correction technique and
probability distribution of bright pixels. AlRuwaili [
7
] proposed an enhancement scheme,
the degraded input image is first converted into an HIS color space, and then color cast
corrections and contrast stretching are performed. Zhi [
8
] restored vivid sand dust images
by using color correction, SVD and the contrast-limited adaptive histogram equalization al-
gorithm. Tri-threshold fuzzy operators are introduced to enhance contrast by Al-Ameen [
9
].
Yan [
10
] enhanced dust images by improving the sub-block partial overlapping histogram
equalization algorithm. Shi [
11
] enhanced images by combining contrast limited adaptive
histogram equalization (CLAHE) and gray world theory. Tensor least square method is
proposed to enhance sand dust image by Xu [
12
]. Cheng [
13
] using white balance and
Sensors 2022, 22, 1918. https://doi.org/10.3390/s22051918 https://www.mdpi.com/journal/sensors
