Image defogging method based on dark channel compensation and atmospheric light value improvement

Abstract

The invention discloses an image defogging method based on dark channel compensation and atmospheric light value improvement, which not only can effectively correct an underestimated dark channel value and weaken the halo effect at the edge of an image scene, but also can accurately obtain the atmospheric light value of an image, so that the recovered image is clearer and more natural, and the details are more abundant. The method comprises the following specific implementation steps: firstly, solving a minimum channel image by utilizing R, G and B color channels of an original image, and obtaining a compensated dark channel image by virtue of a dark channel compensation model; then, calculating the atmospheric light value of the image through the steps of graying processing, quadtree segmentation and the like on the original image; and finally, estimating the image transmissivity by combining the dark channel image and the atmospheric light value, and recovering a fog-free image through an atmospheric scattering model. Experimental results and subjective and objective evaluation prove the feasibility and effectiveness of the method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image defogging method based on dark channel compensation and atmospheric light value improvement. Background technique [0002] In the haze scene, the suspended particles in the atmosphere will inevitably absorb and scatter the light to a certain extent, resulting in a significant decrease in the visibility of outdoor images, leading to image degradation such as a decrease in contrast and color attenuation, affecting the visual observation of the human eye and The normal operation of machine vision equipment, so the study of image defogging methods has very important practical significance. [0003] Image defogging methods are mainly divided into two types, one is the defogging method based on image enhancement, which mainly realizes the clarity of the image by improving the contrast and highlighting the details, but this method ignores the inner connecti...

AI Technical Summary

Problems solved by technology

However, the soft matting technology greatly increases the time complexity and space complexity of the dark channel prior algorithm.

Since then, He et al. have successively proposed guided filtering to replace soft matting, which reduces the complexity of the algorithm, but there is still obvious residual fog in the foreground area; Meng et al. proposed a dehazing algorithm based on boundary constraints. By increasing the constraints of the parameters in the physical model to restore the haze-free image, and by sacrificing a small amount of details to improve the restoration effect, a clearer image is obtained, but the computational complexity of the post-processing operation is relatively large; Zhu et al. observed HSV ( Hue,Saturation,Value) color channel found that there is a linear relationship between the difference between brightness and saturation and the depth of field of the image, and based on this, the prior knowledge of color attenuation was proposed, and the mathematical model of the depth of field information about image saturation and brightness was established. Using the method of supervised learning to solve the depth of field information, so as to realize the defogging of the image, this method is simple and effective, but the transmittance calculated by setting the uniform atmospheric scattering coefficient β of the whole image may easily lead to insufficient defogging degree in local areas

[0005] Among the above dehazing algorithms, the dark channel prior dehazing method proposed by He et al. is well known by more people because of its simplicity and effectiveness. Accuracy and other issues directly affect the overall restoration effect of the image

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