scholarly journals Single image mixed dehazing method based on numerical iterative model and DehazeNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254664
Author(s):  
Wenjiang Jiao ◽  
Xingwu Jia ◽  
Yuetong Liu ◽  
Qun Jiang ◽  
Ziyi Sun

As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical model-based and deep learning-based methods are two competitive methods for single image dehazing, but it is still a challenging problem to achieve fidelity and effectively dehazing simultaneously in real hazy scenes. In this work, a mixed iterative model is proposed, which combines a physical model-based method with a learning-based method to restore high-quality clear images, and it has good performance in maintaining natural attributes and completely removing haze. Unlike previous studies, we first divide the image into different regions according to the density of haze to accurately calculate the atmospheric light for restoring haze-free images. Then, dark channel prior and DehazeNet are used to jointly estimate the transmission to promote the final clear haze-free image that is more similar to the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic datasets and real-world datasets. Moreover, to indicate the universality of the proposed method, we further apply it to the remote sensing datasets, which can also produce visually satisfactory results.

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3896
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.


2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Mohit Kumar Verma ◽  
Permendra Kumar Verma

The enhancement of images is an image processing method that highlights certain image information according to specific needs and, at the same time, weakens or removes unwanted information. In the field of computer and machine vision, haziness and fog lead to degradation of images using different degradation mechanisms, including contrast attenuation, blurring, and degradation of the pixels. This limits machine vision systems efficiency such as video monitoring, target tracking, and recognition. Different dark channel single image dehazing algorithms have been designed quickly and efficiently to address image hazing problems. These algorithms rely on the dark channel theory to estimate the atmospheric light which is a crucial dehazing parameter. In this paper, a review of image dehazing and enhancement has been presented.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 5641-5653 ◽  
Author(s):  
Wencheng Wang ◽  
Faliang Chang ◽  
Tao Ji ◽  
Xiaojin Wu

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.


Author(s):  
Yongpeng Pan ◽  
Zhenxue Chen ◽  
Xianming Li ◽  
Weikai He

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm.


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