Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

Gao, Guxue; Lai, Huicheng; Jia, Zhenhong; Liu, YueQin; Wang, Yali

doi:10.1109/jphot.2020.2975833

Cited by 32 publications

(125 citation statements)

References 37 publications

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“…Many underwater image enhancement methods and some sand-dust image restoration approaches are based on ASM, which is a physical model for haze removal. The red channel prior [27,28] and blue channel prior [25,26] are frequently used in enhancing images. However, inverting the red or blue channel can overenhance the red or blue components.…”

Section: Color Distribution Of Sand-dust Imagementioning

confidence: 99%

“…This bluish result is due to the over-enhancement of a weak blue channel. The undesirable bluish artifact frequently appears in blue channel prior-based methods [25,26] that invert the blue color component.…”

Section: Type Inputmentioning

confidence: 99%

“…This method effectively improved the visibility of sand-dust images, however, the recovered colors showed a dim phenomenon. Recently, Gao et al [25] proposed an ASM-based sand-dust image enhancement method based on reversing the blue channel. The atmospheric light and the transmission map were estimated using this prior.…”

Section: Introductionmentioning

confidence: 99%

“…However, most sand-dust image enhancement methods do not fully consider the characteristics of the color distributions of sand-dust images. The blue channel prior, which inverts the blue color component [25,26], was employed in recent years. This blue channel prior can cause a bluish artifact in restored images because the inverting process of a very weak blue component produces a very strong blue color.…”

Section: Introductionmentioning

confidence: 99%

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Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Park

Eom

2021

IEEE Access

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Outdoor images in sand-dust environments play an adverse role in various remote-based computer vision tasks because captured sand-dust images have severe color casts, low contrast, and poor visibility. However, although sand-dust image restoration is as important as haze removal and underwater image enhancement, it has not been sufficiently studied. In this paper, we present a novel color balance algorithm for sand-dust image enhancement. The aim of the proposed enhancement method is to obtain a coincident chromatic histogram. First, we introduce a pixel-adaptive color correction method using the mean and standard deviation of chromatic histograms. Pixels of each color component are adjusted based on the statistical characteristics of the green component. Second, a green-mean-preserving color normalization technique is presented. However, using the mean of red and blue components as the mean of the green can result in an undesirable output because the red or blue components of many sand-dust images have a narrow histogram with a high peak. To address this problem, we propose a histogram shifting algorithm that makes the red and blue histograms overlap the green histogram as much as possible. Based on this algorithm, bluish or reddish artifacts of the enhanced image can be reduced. Finally, image adjustment is exploited to improve the brightness of the sand-dust image. We performed intensive experiments for various sand-dust images and compared the performance of the proposed method with those of state-of-the-art enhancement methods. The simulation results indicate that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities. INDEX TERMS Sand-dust image enhancement, color normalization, green-mean preserving, maximum overlapped histogram, coincident chromatic histogram.

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Section: Color Distribution Of Sand-dust Imagementioning

confidence: 99%

Section: Type Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Park

Eom

2021

IEEE Access

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“…Shi et al [22] presented a halo-reduced DCP algorithm, which mainly includes three modules: color correction, defogging and contrast enhancement. Very recently, [23] introduced a sand-dust image restoration method based on reversing the blue channel prior. A blue channel and fusion algorithm [24] recovered images by using blue channel technology, and then the fusion algorithm enhanced the contrast, chroma and edge intensity of the images.…”

Section: Related Workmentioning

confidence: 99%

A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

et al. 2020

Self Cite

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The images captured in sand-dust weather have the characteristics of color deviation and low visibility, which seriously affect computer vision systems. To solve the above problems, we propose a fast and effective algorithm to enhance the images captured in sand-dust weather conditions. First, we compensate for the loss value in the blue channel. Then, white balancing technology is used to correct the color of the sand-dust-degraded image. Finally, guided image filtering is used to enhance the image contrast and edge accuracy, and an adaptive method is used to calculate the magnification factor of the detail layer to enhance the image detail information. The experimental results on a large number of sand-dust-degraded images show that the method can effectively recover the fading characteristics of sand-dust-degraded images in a short time and improve the clarity of the images. Experimental results via qualitative and quantitative evaluations demonstrate that the proposed method can significantly improve the images captured during sand-dust weather conditions, and the results are better than those of other methods. INDEX TERMS Sand-dust-degraded image, blue channel compensation, color correction, guided image filtering.

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Automotive Holographic Head‐Up Displays

Skirnewskaja

Wilkinson

2022

Advanced Materials

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Driver's access to information about navigation and vehicle data through in‐car displays and personal devices distract the driver from safe vehicle management. The discrepancy between road safety and infotainment must be addressed to develop safely operated modern vehicles. Head‐up displays (HUDs) aim to introduce a seamless uptake of visual information for the driver while securely operating a vehicle. HUDs projected on the windshield provide the driver with visual navigation and vehicle data within the comfort of the driver's personal eye box through a customizable extended display space. Windshield HUDs do not require the driver to shift the gaze away from the road to attain road information. This article presents a review of technological advances and future perspectives in holographic HUDs by analyzing the optoelectronics devices and the user experience of the driver. The review elucidates holographic displays and full augmented reality in 3D with depth perception when projecting the visual information on the road within the driver's gaze. Design factors, functionality, and the integration of personalized machine learning technologies into holographic HUDs are discussed. Application examples of the display technologies regarding road safety and security are presented. An outlook is provided to reflect on display trends and autonomous driving.

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Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

Cited by 32 publications

References 37 publications

Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

Automotive Holographic Head‐Up Displays

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