Image Dehazing by Incorporating Markov Random Field with Dark Channel Prior

Xu, Hao; Tan, Yibo; Wang, Wenzong; Guo-yu, Wang

doi:10.1007/s11802-020-4003-6

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This proposed haze removal algorithm was tested on various real time environmental images. Xu et al 11 used Markov Random Field for reducing the haze affected contents in single image. The authors used Dark Channel Prior (DCP) technique to detect the haze affected pixels in the source image.…”

Section: Literature Surveymentioning

confidence: 99%

“…Since the amount of scattering depends on the unknown distances of the scene points from the camera and the air‐light is also unknown, it is challenging to remove haze from haze images, especially when there is only a single haze image. Many methods were presented for detecting and removing the haze components by using multiple images 9‐13 . This process required complex algorithm for removing the haze from the image using multiple images.…”

Section: Introductionmentioning

confidence: 99%

“…Many methods were presented for detecting and removing the haze components by using multiple images. [9][10][11][12][13] This process required complex algorithm for removing the haze from the image using multiple images. Also, the average time consumption for removing the haze component from the single image by multiple images is high.…”

mentioning

confidence: 99%

See 2 more Smart Citations

An efficient approach for removing haze from single image using Gaussian pyramidal decomposition

Pandian¹,

Poruran²

2021

Concurrency and Computation

View full text Add to dashboard Cite

The visibility of the image is degraded by haze components which are affected in image during image acquisition process. The haze removal process can be categorized into haze reduction from single image and haze reduction from multiple images. The second case consumes more time and the complexity of algorithm is high. In order to overcome such limitations, this paper proposes an efficient methodology for haze reduction in single acquired image. In this paper, haze detection and removing methodology is proposed using Gaussian pyramidal decomposition. This proposed methodology is tested on both indoor and outdoor haze affected images. The performance of this proposed haze removal algorithm is analyzed with respect to average running time and peak signal to noise ratio, mean square error, mean absolute error, Entropy, and normalized histogram intersection coefficient. K E Y W O R D Sdecomposition, Gaussian pyramidal, haze detection, single image INTRODUCTIONImage surveillance and object detection in an image plays an important role in real world applications. During the acquiring of the images, hazy and foggy particles will present in the image which degrades the quality of the acquired images. The lights scattered by these particles will also affect the luminance intensity of the nearby pixels in an image. [1][2][3][4][5] This will create fading and low contrast pixels in an image. Hence, the visibility of the image is degraded with respect to increasing these scattered components in outdoor or indoor environments. This will create many problems for real time image processing applications such as unmanned vehicle moving system and satellite image system. 6,7 Hence, there is a need for removing the haze components from the acquired image for improving the quality of the image. This will help the computer vision based applications in real time image processing applications. 8 Figure 1(A) shows the haze affected in outdoor images and Figure 1(B) shows the haze affected in indoor images.The performance of computer vision algorithms and advanced image editing algorithms can also be improved. Therefore, haze removal is highly demanded in image processing, computational photography and computer vision applications. Since the amount of scattering depends on the unknown distances of the scene points from the camera and the air-light is also unknown, it is challenging to remove haze from haze images, especially when there is only a single haze image. Many methods were presented for detecting and removing the haze components by using multiple images. [9][10][11][12][13] This process required complex algorithm for removing the haze from the image using multiple images. Also, the average time consumption for removing the haze component from the single image by multiple images is high. In order to overcome such limitations in conventional haze removing process, this paper proposes an efficient methodology for removing the haze components from the single source image only. This method uses Gaussian pyramidal decomposition (GPD) a...

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%