Fast Single Image Haze Removal Scheme Using Self-Adjusting

2019 ◽  
Vol 3 (1) ◽  
pp. 42-57
Author(s):  
Sangita Roy ◽  
Sheli Sinha Chaudhuri
Keyword(s):  
Optical Model ◽  
Classical Problem ◽  
High Contrast ◽  
Single Image ◽  
High Quality ◽  
Pixel Intensity ◽  
Haze Removal ◽  
Real Time Applications ◽  
Bad Weather ◽  
Minimum Intensity

At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness factor to be a ratio of minimum to maximum pixel intensity of the hazy image. Adaptive contrast, extinction coefficient, the maximum visible distance of hazy images as well as dehazed images from each image are evaluated uniquely. The resulting high-quality haze free image with linear computational complexity O(n) is appropriate for real time applications. The effectiveness of the technique is validated by quantitative, and qualitative evaluations.

Single image haze removal using integrated dark and bright channel prior

2018 ◽  
Vol 32 (04) ◽  
pp. 1850051 ◽  
Author(s):  
Dilbag Singh ◽  
Vijay Kumar
Keyword(s):  
Computational Time ◽  
Single Image ◽  
Edge Preservation ◽  
Haze Removal ◽  
Computational Speed ◽  
Dark Channel ◽  
Real Time Applications ◽  
Removal Model ◽  
Bright Channel Prior ◽  
Restoration Model

In recent years, the dark channel prior (DCP) has been proven to be an adequate haze removal model. However, its procedure causes annoying halo and gradient reversal artifacts. To remove these issues, numerous filtering techniques have been designed and integrated with DCP. But, these filters inevitably induce massive computational load and edge degradation problems. In this paper, a novel visibility restoration model is proposed to solve the above-mentioned problems. It utilizes DCP, bright channel prior (BCP) and gain intervention filter. BCP is used to solve the sky-region problem associated with DCP based dehazing. The gain intervention filter is used to improve computational speed and edge preservation. The experimental results reveal that the proposed dehazing model provides better computational time as compared to the existing dehazing techniques. It also provides superior restored images over existing dehazing techniques. More significantly, the designed dehazing model possesses highest potential for real time applications due to its superior restoration results and computational speed.

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.

scholarly journals Single image haze removal considering sensor blur and noise

2013 ◽  
Vol 2013 (1) ◽  
Author(s):  
Xia Lan ◽  
Liangpei Zhang ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Huifang Li
Keyword(s):  
Single Image ◽  
Haze Removal

scholarly journals Single image haze removal using variable fog-weight

2020 ◽  
Vol 1706 ◽  
pp. 012091
Author(s):  
Mohammed Shoaib ◽  
Mohd Mohsin ◽  
Imbeshat Khalid Ansari ◽  
Harshat Maddhesiya ◽  
Upendra Kumar Acharya
Keyword(s):  
Single Image ◽  
Haze Removal

Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region

2017 ◽  
Vol 77 (11) ◽  
pp. 13513-13530 ◽  
Author(s):  
Bo Jiang ◽  
Hongqi Meng ◽  
Jian Zhao ◽  
Xiaolei Ma ◽  
Siyu Jiang ◽  
...  
Keyword(s):  
Single Image ◽  
Color Channel ◽  
Image Filter ◽  
Haze Removal ◽  
Guided Image Filter ◽  
Channel Information ◽  
Self Adaptive
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