Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Al-Ameen, Zohair

doi:10.5815/ijisa.2016.08.02

Cited by 36 publications

(142 citation statements)

References 22 publications

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“…Wang et al [11] transformed the sand-dust-degraded image into the CIELAB space, processed the color component with color correction and contrast stretching, and processed the brightness component with local Laplace filtering. The employed algorithm in [12] can rapidly process sand-dustdegraded images by using the optimized fuzzy enhancement operator. Yang et al [13] proposed an optical compensation method to correct the sand-dust image color cast, and used the linear constraint of the Gaussian function to optimize the transmission.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…The enhancement simulation results are shown in Figs. 4, 5, 6, and 7, among which (a) is the original image, (b) is enhanced using Dynamic Stretching-based Brightness Preservation [16] (DSBP), (c) is the enhancement effect via Tuned Fuzzy Intensification operators [17] (Fuzzy_INT), (d) is the image enhanced by NSCT adaptive enhancement [8], (e) is the enhancement result with NSST [10], and (f ) is the performance of the proposed algorithm.…”

Section: Simulation and Evaluationmentioning

confidence: 99%

Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

Tong

2019

J Wireless Com Network

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Acquiring clear images is a requisite in visual sensor networks. Image enhancement is an effective way to improve image quality. In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST) to nonlinearly enhance the images captured by visual sensors. The components of different scales after NSST multi-scale decomposition are processed by nonlinear models with different thresholds. The thresholds of the enhanced model are determined by the local standard deviation of PST feature map. The noise is well suppressed, and the detail features are enhanced obviously. Experiments show that the proposed algorithm can improve image distortion, clear details, and enhance image contrast effectively.

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“…The other derived image is obtained using a fuzzy intensification operator which is mainly used to restore the colour that was lost because of the air-light as described in equation (1). In Fuzzy intensification as explained in [16] (12) and Membership for blue channel Mf B, is specified in Eq.…”

Section: Derived Input Image(2) Using Fuzzy Intensificationmentioning

confidence: 99%

“…So the membership function will convert the pixels in the range 0 to 1. Further, fuzzy intensification is applied as explained in [16], is shown in Eq. 14for the red channel.…”

Section: Derived Input Image(2) Using Fuzzy Intensificationmentioning

confidence: 99%

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Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

T.N

Shankar

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images.

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Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators

Cited by 36 publications

References 22 publications

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

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

Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

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