scholarly journals Dehazing with Offset Correction and a Weighted Residual Map

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1419
Author(s):  
Chang Su ◽  
Wensheng Wang ◽  
Xingxiang Zhang ◽  
Longxu Jin
Keyword(s):  
Image Quality ◽  
Estimation Method ◽  
Expected Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Information ◽  
Dark Channel ◽  
Offset Correction ◽  
Transmission Estimation

In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to correct the transmission. However, this correction may neglect scene difference and affect the clarity of the recovered images. Therefore, a weighted residual map is used to enhance contrast and recover more information. Experimental results demonstrate that our algorithm can effectively lessen color oversaturation and restore images with enhanced details. This algorithm provides a more accurate transmission estimation method that can be used with a weighted residual map to eliminate haze and improve contrast.

scholarly journals An image dehazing algorithm based on double priors constraint

2018 ◽  
Vol 189 ◽  
pp. 04009
Author(s):  
Kun Liu ◽  
Shiping Wang ◽  
Linyuan He ◽  
Duyan Bi ◽  
Shan Gao
Keyword(s):  
Standard Deviation ◽  
Mean Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Color Distortion ◽  
Local Media ◽  
Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

scholarly journals IMAGE FAST DEHAZING ALGORITHM BASED ON DARK CHANNEL PRIOR IMPROVEMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 585-589
Author(s):  
J. W. Li ◽  
L. Liu ◽  
J. W. Jiang ◽  
Y. Hu ◽  
X. Q. Han ◽  
...  
Keyword(s):  
Gamma Function ◽  
Information Entropy ◽  
Experimental Results ◽  
Guided Filter ◽  
Dark Channel Prior ◽  
Two Dimensional ◽  
Image Dehazing ◽  
Running Time ◽  
Dark Channel ◽  
Intensity Image

Abstract. Aiming at the long-running time and the defogging image darkening problem in the dark channel prior algorithm, a fast deaeration algorithm based on the guided filter and improved two-dimensional gamma function for dark channel prior image is proposed. The algorithm uses the guided filter instead of the soft matting to obtain the image transmittance. The summation operation in the window replaces the quadrature operation in the window to reduce the complexity of the algorithm, and the image is processed by the two-dimensional gamma function. The brightness is adjusted to increase the brightness of the dark areas of the image, improve the contrast of the image, and enhance the image's performance in detail. The experimental results show that compared with the dark channel prior defogging algorithm and other image dehazing algorithms, the image fast dehazing algorithm based on dark channel prior improvement has high effective detail intensity, image information entropy and average gradient. The running time of the dark channel prior defogging algorithm is reduced, which effectively solves the long running time and the defogging image darkness problem of the dark channel prior defogging algorithm and has good robustness, and improves the quality and display effects of defogging image.

scholarly journals AN IMPROVED AERIAL REMOTE SENSING IMAGE DEFOGGING METHOD BASED ON DARK CHANNEL PRIOR INFORMATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1025-1030
Author(s):  
Z. Zhang ◽  
W. Feng ◽  
T. Wang ◽  
Y. Zhang ◽  
L. Ding
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Prior Information ◽  
Remote Sensing Image ◽  
Dark Channel Prior ◽  
Visual Effect ◽  
Image Defogging ◽  
Dark Channel ◽  
Aerial Remote Sensing ◽  
Color Cast

Aerial remote sensing image is widely used due to its high resolution, abundant information and convenient processing. However, its image quality is easily influenced by clouds and fog. In recent years, fog and haze air pollution is becoming more and more serious in the north of China and its influence on aerial remote sensing image quality is especially obvious. Considering the characters that aerial remote image is usually in huge amount of data and seldom covers sky area, this paper proposes an improved aerial remote sensing image defogging method based on dark channel prior information. First, a 2 % linear stretching is applied to eliminate the haze offset effect and provide a better initial value for later defogging processing. Then the dark channel prior image is obtained by calculating the minimum values of r, g, b channels of each pixel directly. Subsequently, according to the particularity of aerial image, the adaptive threshold t0 is set up to improve the defogging effect. Finally, to improve the color cast phenomenon, a way called automatic color method is introduced to enhance the visual effect of defogged image. Experiments are performed on normal image in fog and on aerial remote sensing image in fog. Experimental results prove that the proposed method can obtain the defogged image with better visual effect and image quality. Moreover, the improved method significantly balances the color information in the defogged image and efficiently avoids the color cast phenomenon.

scholarly journals Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

2019 ◽  
Vol 8 (12) ◽  
pp. 3015-3020
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Blocking Artifacts ◽  
Image Dehazing ◽  
Data Set ◽  
Dark Channel ◽  
Atmospheric Light

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.

scholarly journals Coastal Visibility Distance Estimation Using Dark Channel Prior and Distance Map Under Sea-Fog: Korean Peninsula Case

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4432
Author(s):  
Tae Wuk Bae ◽  
Jin Hyun Han ◽  
Kuk Jin Kim ◽  
Young Taeg Kim
Keyword(s):  
Estimation Method ◽  
Distance Estimation ◽  
Local Limit ◽  
Wide Area ◽  
Dark Channel Prior ◽  
Distance Map ◽  
Sea Fog ◽  
Visibility Distance ◽  
Sensor Structure ◽  
Dark Channel

Commercial visibility sensors among meteorological sensors estimate the visibility distance based on transmission, backward scattering, and forward scattering principle. These optical visibility sensors yield comparatively accurate local visibility distance. However, it is still difficult to obtain comprehensive visibility information for a wide area, such as the coast or harbor due to the sensor structure using straightness and scattering properties of light. In this paper, we propose a novel visibility distance estimation method using dark channel prior (DCP) and distance map based on a camera image. The proposed method improves the local limit of optical visibility sensor and detects the visibility distance of a wide area more precisely. First, the dark channel for an input sea-fog image is calculated. The binary transmission image is obtained by applying a threshold to the estimated transmission from the dark channel. Then, the sum of the distance values of pixels, corresponding to the sea-fog boundary, is averaged, in order to derive the visibility distance. This paper also proposes a novel air-light and transmission estimation technique in order to extract the visibility distance for an abnormal sea-fog image, including any light source, such as sunlight, reflection light, and illumination light, etc. The estimated visibility distance was compared with optical visibility distance of an optical visibility sensor and their agreement was evaluated.

Image Defogging Based on Improved Guided Image Filtering

2014 ◽  
Vol 536-537 ◽  
pp. 121-126
Author(s):  
Peng Fei Shen ◽  
Jie Yang ◽  
Yuan Yi Xiong
Keyword(s):  
Experimental Results ◽  
Dark Channel Prior ◽  
Improved Method ◽  
Visual Effect ◽  
Color Distortion ◽  
Image Defogging ◽  
Dark Channel ◽  
Guided Filtering ◽  
Guided Image Filtering ◽  
Estimated Error

In this paper, we analyze the principles of the dark channel prior based on guided filtering image algorithm to defog, pointing out the shortcomings and derive an improved method. Dark channel prior principle is established in the absence of bright areas, which not satisfied Dark channel prior, and thus, the transmittance of the bright areas is estimated error, which will case color distortion of defogged image. By introducing a tolerancemechanism refining the transmittance, the algorithm can effectively handle such problem to overcome the color distortion in bright areas using dark channel prior. Experimental results show that this modification is substantial practicable to restore image, at the same time eliminates the color distortion, significantly improve the visual effect.

Single Image Haze Removal Based on Dark Channel Prior Applied on Air Duct Robot

2012 ◽  
Vol 220-223 ◽  
pp. 1307-1310
Author(s):  
Peng Fei Yang ◽  
Wei Sun ◽  
Sheng Nan Liu ◽  
Ming Hua Ouyang
Keyword(s):  
Experimental Results ◽  
Good Effect ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Cleaning Robots ◽  
Duct Cleaning

The rule of dark channel prior has made significant effect in outdoor image dehazing. The camera on air duct cleaning robots will capture foggy pictures when they are working because of raised dust, these foggy pictures have serious impact on the robot cleaning work. According to the characteristics of pictures in air ducts, we remove haze of these images based on dark channel prior, experimental results show that this method has good effect.

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