Remote sensing image uneven haze removal based on correction of saturation map

Yang, Shun-Jie; Cui, Guangmang; Zhao, Jincheng

doi:10.1117/1.jei.30.6.063033

Cited by 1 publication

(8 citation statements)

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“…The resulting images generated by these atmospheric conditions are the images with low contrast, distorted colors, and reduced original scenes. Image enhancement with depth map estimation 9 is a very active research area to provide the basic framework for haze removal, as shown in Fig. 2.…”

Section: Aspects Of Image Dehazingmentioning

confidence: 99%

“…The scene properties can be shading function or contrast-based cost function. Once depth information is estimated, it is easier to restore the image using the fog model 9 …”

Section: Aspects Of Image Dehazingmentioning

confidence: 99%

“…In the direction of θ is the observer, the radiant intensity Iðθ; λÞ of the unit volume equals to of image dehazing methods leverages conventional and non-learning-based image degradation processes for depth information. 9,11 These approaches have captured multiple real-time images under different weather conditions of the same scene to enhance the actual image by reversing the degradation process. [9][10][11][12][13] This paper presents a review of the prior research on various techniques of image enhancement and restoration employed to ensure visual clarity under hazy conditions.…”

Section: Mathematical Expression Of Atmospheric Scattering For the Cr...mentioning

confidence: 99%

“… – 5 , 8 Airlight estimation is essential for determining the depth information within hazy images, relying on scene-specific characteristics. The initial stage of any haze removal method typically involves contrast enhancement and image restoration 9 , 10 . The second category of image dehazing methods leverages conventional and non-learning-based image degradation processes for depth information 9 , 11 .…”

Section: Introductionmentioning

confidence: 99%

“…The initial stage of any haze removal method typically involves contrast enhancement and image restoration 9 , 10 . The second category of image dehazing methods leverages conventional and non-learning-based image degradation processes for depth information 9 , 11 . These approaches have captured multiple real-time images under different weather conditions of the same scene to enhance the actual image by reversing the degradation process 9 …”

Section: Introductionmentioning

confidence: 99%

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Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

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Section: Aspects Of Image Dehazingmentioning

confidence: 99%

“…The scene properties can be shading function or contrast-based cost function. Once depth information is estimated, it is easier to restore the image using the fog model 9 …”

Section: Aspects Of Image Dehazingmentioning

confidence: 99%