scholarly journals Haze Removal Based on Refined Transmission Map for Aerial Image Matching

2021 ◽  
Vol 11 (15) ◽  
pp. 6917
Author(s):  
Yogendra Rao Musunuri ◽  
Oh-Seol Kwon
Keyword(s):  
Image Matching ◽  
Aerial Image ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Image Blurring ◽  
Degraded Image ◽  
Blurred Image ◽  
Novel Strategy ◽  
Local Feature Extraction

A novel strategy is proposed to address block artifacts in a conventional dark channel prior (DCP). The DCP was used to estimate the transmission map based on patch-based processing, which also results in image blurring. To enhance a degraded image, the proposed single-image dehazing technique restores a blurred image with a refined DCP based on a hidden Markov random field. Therefore, the proposed algorithm estimates a refined transmission map that can reduce the block artifacts and improve the image clarity without explicit guided filters. Experiments were performed on the remote-sensing images. The results confirm that the proposed algorithm is superior to the conventional approaches to image haze removal. Moreover, the proposed algorithm is suitable for image matching based on local feature extraction.

scholarly journals Transmission Estimation by Complex Assumption with Occlusion Handling

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang ◽  
Mingzhu Zhu ◽  
Bingwei He
Keyword(s):  
Optimal Solution ◽  
Image Sensor ◽  
Natural Images ◽  
Single Image ◽  
Occlusion Handling ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Transmission Estimation ◽  
Single Image Dehazing

Transmission estimation is a critical step in single-image dehazing. The estimate of each pixel describes the portion of the scene radiance that is degraded by hazing and finally reaches the image sensor. Transmission estimation is an underconstrained problem, and, thus, various assumptions, priors, and models are employed to make it solvable. However, most of the previous methods did not consider the different assumptions simultaneously, which, therefore, did not correctly reflect the previous assumptions in the final result. This paper focuses on this problem and proposes a method using an energy function that clearly defines the optimal transmission map and combines the assumptions from three aspects: fidelity, smoothness, and occlusion handling, simultaneously. Fidelity is measured by a novel principle derived from the dark channel prior, smoothness is described by the assumption of piecewise smoothening, and occlusion handling is achieved based on a new proposed feature. The transmissions are estimated by searching for the optimal solution of the function that can retain all the employed assumptions simultaneously. The proposed method is evaluated on the synthetic images of two datasets and various natural images. The results show that there is remarkable fidelity and smoothness in the transmission and that a good performance is exhibited for haze removal.

scholarly journals Joint Image Deblurring and Matching with Blurred Invariant-Based Sparse Representation Prior

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuanjie Shao ◽  
Nong Sang ◽  
Juncai Peng ◽  
Changxin Gao
Keyword(s):  
Sparse Representation ◽  
Image Matching ◽  
Image Deblurring ◽  
Image Feature ◽  
Zernike Moment ◽  
Invariant Space ◽  
Pseudo Zernike Moment ◽  
Image Blurring ◽  
Blurred Image

Image matching is important for vision-based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to deal with this problem by utilizing a two-stage framework—first resorting to image deblurring and then performing image matching, which is effective but depends heavily on the quality of image deblurring. An emerging way to resolve this dilemma is to perform image deblurring and matching jointly, which utilize sparse representation prior to explore the correlation between deblurring and matching. However, these approaches obtain the sparse representation prior in the original pixel space, which do not adequately consider the influence of image blurring and thus may lead to an inaccurate estimation of sparse representation prior. Fortunately, we can extract the pseudo-Zernike moment with blurred invariant from images and obtain a reliable sparse representation prior in the blurred invariant space. Motivated by the observation, we propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior (JDM-BISR), which obtains the sparse representation prior in the robust blurred invariant space rather than the original pixel space and thus can effectively improve the quality of image deblurring and the accuracy of image matching. Moreover, since the dimension of the pseudo-Zernike moment is much lower than the original image feature, our model can also increase the computational efficiency. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art blurred image matching approach.

Aerial image dehazing using improved dark channel prior

2020 ◽  
Vol 28 (6) ◽  
pp. 1387-1394
Author(s):  
韩昊男 HAN Hao-nan ◽  
钱锋 QIAN Feng ◽  
吕建威 LJian-wei ◽  
张葆 ZHANG Bao
Keyword(s):  
Aerial Image ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel

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.

scholarly journals Haze Removal System using Dark Channel Prior

2021 ◽  
Vol 9 (VI) ◽  
pp. 301-309
Author(s):  
Disha M. Jaiswal
Keyword(s):  
Road Traffic ◽  
Winter Season ◽  
Road Transport ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Haze Removal ◽  
The Road ◽  
Dark Channel ◽  
Atmospheric Light ◽  
Removal System

Mostly in winter season, the Northern area of India is mostly affected due to heavy haze. The road traffic and air traffic is affected due to poor visibility. According to the survey of Ministry of Road Transport and Highways of India, the number of accident due to poor visibility increasing every year. Hence there is need of robust algorithm to enhance the visibility of the camera feed. In the proposed approach, image dehazing algorithm has been present using dark channel prior. The proposed algorithm is developed for outdoor images. The proposed system processed the image through dark channel prior, estimation of atmospheric light, estimation of transmission and scene radiance. The proposed system achieved the promising results on O-Haze dataset.

scholarly journals Dual-Path in Dual-Path Network for Single Image Dehazing

2019 ◽  
Author(s):  
Aiping Yang ◽  
Haixin Wang ◽  
Zhong Ji ◽  
Yanwei Pang ◽  
Ling Shao
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Single Image ◽  
Image Dehazing ◽  
Popular Approach ◽  
Haze Removal ◽  
Image Blurring ◽  
Atmospheric Light ◽  
Single Image Dehazing ◽  
Structural Layer

Recently, deep learning-based single image dehazing method has been a popular approach to tackle dehazing. However, the existing dehazing approaches are performed directly on the original hazy image, which easily results in image blurring and noise amplifying. To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path network) framework by employing a hierarchical dual path network. Specifically, the first-level dual-path network consists of a Dehazing Network and a Denoising Network, where the Dehazing Network is responsible for haze removal in the structural layer, and the Denoising Network deals with noise in the textural layer, respectively. And the second-level dual-path network lies in the Dehazing Network, which has an AL-Net (Atmospheric Light Network) and a TM-Net (Transmission Map Network), respectively. Concretely, the AL-Net aims to train the non-uniform atmospheric light, while the TM-Net aims to train the transmission map that reflects the visibility of the image. The final dehazing image is obtained by nonlinearly fusing the output of the Denoising Network and the Dehazing Network. Extensive experiments demonstrate that our proposed DPDP-Net achieves competitive performance against the state-of-the-art methods on both synthetic and real-world images.

scholarly journals A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 73330-73339 ◽  
Author(s):  
Jehoiada Jackson ◽  
She Kun ◽  
Kwame Obour Agyekum ◽  
Ariyo Oluwasanmi ◽  
Parinya Suwansrikham
Keyword(s):  
Rayleigh Scattering ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

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.

Hybrid single image dehazing with bright channel and dark channel priors

2017 ◽  
Author(s):  
Jehoiada Jackson ◽  
Oluwasanmi Ariyo ◽  
Kingsley Acheampong ◽  
Maxwell Boakye ◽  
Enoch Frimpong ◽  
...  
Keyword(s):  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing
Sign in / Sign up

Export Citation Format

Share Document