Mr. Shangyou Xie Profile

Shangyou Xie

at Jiangnan Univ.

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Proceedings Article | 1 August 2022 Paper

Image dehazing algorithm based on color correction and channel compensation prior

Shangyou Xie, Jun Zhang

Proceedings Volume 12257, 122571E (2022) https://doi.org/10.1117/12.2640094

KEYWORDS: Image processing, Image transmission, Image restoration, Atmospheric modeling, Image quality, Distortion, Image filtering, Air contamination, Image enhancement, Scattering

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The dark channel prior is a statistical conclusion based on outdoor haze-free images and it is widely used in haze removal because of its effectiveness. However, it does not work well when dealing with images containing sky areas. The reason is values of R,G,B channels are very high in this region , i.e. the dark channel prior does not hold . Therefore, the transmission of this region is overestimated, resulting in serious halo artifacts and noise amplification in the recovered image. To solve this problem, an image dehazing algorithm based on the inverse channel compensation prior is proposed in this paper. According to observation and experimental verification, for haze-free images, when the pixels in the sky region of the image have higher values in a certain channel, their inverse channel values are lower even tend to be close to zero. Therefore, when finding the dark channel of the image, the inverse channel is considered for compensation so that the pixels in the failed sky region also satisfy the dark channel prior assumption. It is worth noting that the method based on the inverse channel compensation in this paper can obtain the accurate transmittance, and the sky region will not produce distortion and noise amplification even if the lower constraint on transmission is not set . In addition, a simple and effective color cast judgment and adaptive correction method is proposed based on the characteristics of atmospheric light , and adaptive color correction if necessary, which makes the method in this paper effective for the enhancement of sand dust images , extending the applicable scenarios of the algorithm.

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