scholarly journals Image Dehazing Technique Based On DWT Decomposition and Intensity Retinex Algorithm

2019 ◽  
pp. 116-122
Author(s):  
Sunita Shukla ◽  
Silky Pareyani
Keyword(s):  
Median Filter ◽  
Weather Conditions ◽  
Input Image ◽  
Single Image ◽  
Distance Information ◽  
Haze Removal ◽  
Imaging Model ◽  
Single Input ◽  
Adjacent Pixel ◽  
Different Depths

Conventional designs use multiple image or single image to deal with haze removal. The presented paper uses median filer with modified co-efficient (16 adjacent pixel median) and estimate the transmission map and remove haze from a single input image. The median filter prior(co-efficient) is developed based on the idea that the outdoor visibility of images taken under hazy weather conditions seriously reduced when the distance increases. The thickness of the haze can be estimated effectively and a haze-free image can be recovered by adopting the median filter prior and the new haze imaging model. Our method is stable to image local regions containing objects in different depths. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly. Our method due to its fast speed and the good visual effect is suitable for real-time applications. This work confirms that estimating the transmission map using the distance information instead the color information is a crucial point in image enhancement and especially single image haze removal.

Haze Removal for a Single Image Using Adaptive Template Dark Channel Prior

2014 ◽  
Vol 543-547 ◽  
pp. 2480-2483
Author(s):  
Jing Zhang ◽  
Wei Dong ◽  
Juan Li ◽  
Xu Ning Liu
Keyword(s):  
Input Image ◽  
Dark Channel Prior ◽  
Edge Region ◽  
Single Image ◽  
Haze Removal ◽  
Flat Region ◽  
Imaging Model ◽  
Texture Region ◽  
Dark Channel ◽  
Single Input

In this paper, we propose an adaptive template method based on the dark channel prior. The method combines with the haze imaging model to haze removal for a single image. This method can effectively remove haze from a single input image. According to the characteristics of the image itself and the haze removal effect of the different template we divide the input image into flat region, edge region and texture region. Then, select the lager size template dispose the flat region and use midrange or minitype template dispose the edge region and texture area. Experimental results demonstrate that the proposed algorithm has very good performance for fog removal and retains the image details more effectively.

Single image haze removal based on fusion darkness channel prior

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740037 ◽  
Author(s):  
Xifang Zhu ◽  
Ruxi Xiang ◽  
Feng Wu ◽  
Xiaoyan Jiang
Keyword(s):  
Image Quality ◽  
Channel Model ◽  
Input Image ◽  
The Other ◽  
Fusion Method ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Correlation Information ◽  
Atmospheric Scattering Model

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods.

scholarly journals Improved Single Haze Removal using Weighted Filter and Gaussian-Laplacian

2020 ◽  
Vol 8 (2) ◽  
pp. 26-31
Author(s):  
Ajeeta Singh Bhadoria ◽  
Vandana Vikas Thakre
Keyword(s):  
Median Filter ◽  
Depth Map ◽  
Vital Role ◽  
Computer Applications ◽  
Color Channel ◽  
High Quality ◽  
Haze Removal ◽  
Imaging Model ◽  
Laplacian Filter ◽  
Bad Weather

Generally computer applications use digital images. Digital image plays a vital role in the analysis and explanation of data, which is in the digital form. Images and videos of outside scenes are generally affected by the bad weather environment such as haze, fog, mist etc. It will result in bad visibility of the scene caused by the lack of quality. This paper exhibits a study about various image defogging techniques to eject the haze from the fog images caught in true world to recuperate a fast and enhanced nature of fog free images. In this paper, we propose a simple but effective the weighted median (WM) filter was first presented as an overview of the standard median filter, where a nonnegative integer weight is assigned to each position in the filter window image .Gaussian and laplacian pyramids are applying Gaussian and laplacian filter in an image in cascade order with different kernel sizes of gaussian and laplacian filter .The dark channel prior is a type of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one-color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a by-product of haze removal and Calculate the PSNR and MSE of three sample images.

Single image haze removal with approximate radiance darkness prior

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840086 ◽  
Author(s):  
Ruxi Xiang ◽  
Feng Wu
Keyword(s):  
Input Image ◽  
Experimental Results ◽  
Single Image ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Dark Channel ◽  
Atmospheric Scattering Model

In this paper, we propose a novel and effective method for removing haze based on a single image, which firstly computes the dark channel of the estimated radiance image by decomposing the dark channel of the haze input image, and the method then estimates the transmission map of the input image. Finally, the scene radiance image is restored by the classical atmospheric scattering model. Experimental results show that the proposed method outperforms He et al.’s method in terms of haze removal.

scholarly journals Color Attenuation Prior (CAP) for Single Image Dehazing

2018 ◽  
Vol 7 (02) ◽  
pp. 23578-23584
Author(s):  
Miss. Anjana Navale ◽  
Prof. Namdev Sawant ◽  
Prof. Umaji Bagal
Keyword(s):  
State Of The Art ◽  
Depth Map ◽  
Depth Information ◽  
Single Image ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Ill Posed ◽  
Single Input ◽  
Scene Depth

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.

A Modified Haze Removal Algorithm Using Dark Channel Prior

2012 ◽  
Vol 457-458 ◽  
pp. 1397-1402
Author(s):  
Xiao Tian Wu ◽  
Xing Hao Ding ◽  
Quan Xiao
Keyword(s):  
Adaptive Algorithm ◽  
Bilateral Filter ◽  
Input Image ◽  
Dark Channel Prior ◽  
Edge Preserving ◽  
Haze Removal ◽  
Dark Channel ◽  
Color Shift ◽  
Single Input ◽  
Channel Image

In this paper, we propose a new algorithm to remove haze from a single input image. Based on the Dark Channel Prior proposed by He [1], we exploit the Gauss Bilateral Filter and the min operation to obtain an edge-preserving dark channel image, which is non-iterative, requires less time. We further utilize this dark channel image to extract the estimation of medium transmission, and finally recover a haze-free image from that. Furthermore, we use a self-adaptive algorithm to set the haze parameters to solve the color shift problem for large sky region. Experiments demonstrate our algorithm can effectively remove haze from a foggy image while keep edges sharp.

scholarly journals A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

2019 ◽  
Vol 9 (17) ◽  
pp. 3443 ◽  
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Dynamic Range ◽  
Median Filter ◽  
Hardware Verification ◽  
Floating Point ◽  
Single Image ◽  
Haze Removal ◽  
Narrow Dynamic Range ◽  
Computation Error

This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.

Single image haze removal based on two steps

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740038
Author(s):  
Ruxi Xiang ◽  
Xifang Zhu ◽  
Feng Wu
Keyword(s):  
Input Image ◽  
Experimental Results ◽  
Filter Method ◽  
Ambient Light ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Novel Method ◽  
Realistic Scenes ◽  
Atmospheric Scattering Model

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods.

Haze removal with automatic recovery of atmospheric light

2019 ◽  
Vol 34 (01n03) ◽  
pp. 2040064
Author(s):  
Ruxi Xiang ◽  
Xifang Zhu ◽  
Feng Wu
Keyword(s):  
Color Constancy ◽  
Weather Condition ◽  
Input Image ◽  
Statistical Characteristics ◽  
User Input ◽  
Haze Removal ◽  
Salient Region ◽  
Imaging Model ◽  
Bad Weather ◽  
Atmospheric Light

Most haze removal methods are based on the classical haze imaging model, which can effectively describe the image from the haze, where one of the key factors is the atmospheric light. Existing methods either depend on the user input or keep to the color constancy. In this paper, we present a new and effective method for computing the atmospheric light based on the salient region of the input haze image. The method computes the salient map and the darkness channel map of the input image and adaptively selects the region of the atmospheric light according to the assumption that the atmospheric light does not belong to the salient region of the input image. Then, the atmospheric light is estimated by combining the statistical characteristics between the darkness channel and the region of the atmospheric light. Finally, the transmission map is computed and refined by the weight-guided filter and the clear image without the haze is produced. Compared with some existing methods, a number of experimental results show that the proposed method can not only accurately recover the atmospheric light, but also effectively remove the haze from bad weather condition and improve the image quality such as the contrast, sharpness and color.

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