scholarly journals Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoqiang Wu ◽  
Yiran Fu ◽  
Quanxing Zha ◽  
Aidong Chen ◽  
Hongyuan Jing
Keyword(s):  
Weather Conditions ◽  
Quality Analysis ◽  
Dark Channel Prior ◽  
Ambient Light ◽  
Low Contrast ◽  
Atmospheric Scattering ◽  
Optimisation Algorithm ◽  
Dark Channel ◽  
Regional Similarity ◽  
Image Details

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.

scholarly journals A Real-Time Haze Removal & Mosaicing using Rapid Prototype Hardware

2020 ◽  
Vol 9 (10) ◽  
pp. 29-34
Keyword(s):  
Real Time ◽  
Rapid Prototype ◽  
Field Of View ◽  
Corner Detection ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Whole Process ◽  
Dark Channel ◽  
Information Fidelity ◽  
Initial Focus

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.

scholarly journals Image Defogging Framework Using Segmentation and the Dark Channel Prior

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 285
Author(s):  
Sabiha Anan ◽  
Mohammad Ibrahim Khan ◽  
Mir Md Saki Kowsar ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Homogeneous Region ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Color Information ◽  
Visual Effects ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Processing Step ◽  
Dark Channel

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

scholarly journals Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

2021 ◽  
Vol 2066 (1) ◽  
pp. 012050
Author(s):  
Hao Chen ◽  
Hongsen He ◽  
Xinghua Feng
Keyword(s):  
Image Enhancement ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Underwater Environment ◽  
Underwater Image ◽  
Blue Channel ◽  
Dark Channel ◽  
Green Background ◽  
Enhancement Method

Abstract Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

scholarly journals Region Adaptive Single Image Dehazing

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1438
Author(s):  
Changwon Kim
Keyword(s):  
Real Data ◽  
Weather Conditions ◽  
Data Sets ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Texture Region ◽  
Adverse Weather ◽  
Dark Channel ◽  
Single Image Dehazing

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.

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 Underwater image enhancement algorithm based on dark channel prior and underwater imaging model

2021 ◽  
Vol 336 ◽  
pp. 06033
Author(s):  
Zhengping Sun ◽  
Fubing Li ◽  
Yuying Yang
Keyword(s):  
Image Enhancement ◽  
Dark Channel Prior ◽  
Underwater Imaging ◽  
Low Contrast ◽  
Underwater Environment ◽  
Imaging Model ◽  
Underwater Image ◽  
Dark Channel ◽  
Color Compensation ◽  
Enhancement Method

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.

scholarly journals Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1220 ◽  
Author(s):  
Ho Sang Lee ◽  
Sang Whan Moon ◽  
Il Kyu Eom
Keyword(s):  
Image Enhancement ◽  
Correction Method ◽  
Color Correction ◽  
Water Medium ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
White Balance ◽  
Underwater Image ◽  
Corrected Image ◽  
Dark Channel

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

Research on Images Restoration Method under the Foggy Environment

2014 ◽  
Vol 1070-1072 ◽  
pp. 2037-2040
Author(s):  
Hui Liu ◽  
Xue Bin Liu
Keyword(s):  
Histogram Equalization ◽  
Paper Analysis ◽  
Dark Channel Prior ◽  
Atmospheric Scattering ◽  
Adaptive Histogram Equalization ◽  
Dark Channel ◽  
Histogram Similarity ◽  
Restoration Method ◽  
Degraded Images

Because of the atmospheric scattering phenomenon, weather atmospheric degraded images captured in foggy environment have poor contrast and visibility, it has seriously affected the quality of the images. So this paper analysis and find something different between the dark channel prior and the interpolating self-adaptive histogram equalization method based on physical and non-physical model. And using the histogram similarity evaluation is evaluated on them. Finally, further discussion are indicated on techniques challenges and future development.

scholarly journals SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 355-360 ◽  
Author(s):  
Y. Zhai ◽  
D. Ji
Keyword(s):  
Weather Conditions ◽  
Input Image ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Second Gradient ◽  
Tv Regularization ◽  
Dark Channel ◽  
Single Input ◽  
Computer Vision Systems ◽  
Degraded Images

Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, the famous dark channel prior, a statistics of the haze-free outdoor images, can be used to estimate the thickness of the haze; and second, gradient prior law of transmission maps, which is based on dark channel prior. By integrating these two priors, to estimate the unknown scene transmission map is modeled into a TV-regularization optimization problem. The experimental results show that the proposed approach can effectively improve the visibility and keep the details of fog degraded images in the meanwhile.

Denoising, Edge Aware Restoration and Enhancement of Single Shallow Coastal Water Image

2021 ◽  
pp. 2250009
Author(s):  
Mary Cecilia ◽  
S. Sakthivel Murugan
Keyword(s):  
Poor Quality ◽  
Single Shot ◽  
Dark Channel Prior ◽  
Turbid Water ◽  
Edge Preserving ◽  
Low Contrast ◽  
Underwater Image ◽  
Dark Channel ◽  
Restoration Method ◽  
Underwater Image Processing

The dissolved particles and their resultant scattering are the underlying cause of low contrast and blur thereby producing poor quality underwater images. Single-shot shallow coastal underwater images are much in need of the preprocessing steps viz. image enhancement and restoration. The underwater image processing operations like classification, object detection and computer vision require an enhancement/restoration preprocessing. The paper aims to restore the visibility of objects in turbid water images with its effective edge aware restoration cum enhancement model. The restricted rolling guidance filter on the DCP-based restoration method produces better edge aware restoration and denoised output image. The model-based (MB) dark channel prior (DCP) along with an edge emphasis and contrast enhancement achieves the essential dehazing and improvement in contrast for the heavily blurred underwater images. The subjective and objective projections are an evidence for the same. This edge preserving and denoising nature of the model is also exhibited with comparisons made with promising algorithms over the decade.

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