Image defogging method based on improved dynamic atmospheric scattering coefficient function

Abstract

The invention discloses an image defogging method based on an improved dynamic atmospheric scattering coefficient function, and the method comprises the steps: solving a minimum channel image throughemploying R, G and B color channels of an original image, and calculating an atmospheric light value A of a foggy day image through a quadtree segmentation method; calculating the depth of field d (x)of the image by using a nonlinear color attenuation prior model, and filtering noise information in the depth of field d (x) of the image through minimum filtering, smooth filtering and guided filtering to obtain the final depth of field dr (x) of the image; and finally, in combination with the improved dynamic atmospheric scattering coefficient function beta (x) and the image depth of field dr (x) obtained through final processing, calculating atmospheric transmissivity t (x), and recovering a fog-free image through an atmospheric scattering model. The method not only can solve the problem of inaccurate transmissivity estimation caused by constant atmospheric scattering coefficient, but also can effectively solve the phenomenon of color distortion of the sky area of the image, so that the defogging effect of the image is clearer and more thorough, and the color of the scenery is more natural and real.

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 an improved dynamic atmospheric scattering coefficient function. Background technique [0002] Smog is a common natural phenomenon on land and sea. In severe weather such as fog and haze, aerosols and solid particles suspended in the atmosphere will absorb and scatter light to a certain extent, degrading the images captured by the shooting equipment, usually including low contrast and low visibility, which will Can seriously affect the visual system, especially the visible light vision system. Since the sharpness of the degraded image is seriously reduced, it will be a huge difficulty and challenge for the subsequent automatic processing of the image. Therefore, it is of great practical significance to develop a fast and effective adaptive image defogging method for the automatic processing of images and videos. . [0003...

AI Technical Summary

Problems solved by technology

For example, He et al. proposed to use guided filtering instead of 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. The constraints of the parameters in the physical model are used to restore the fog-free image. 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. found by observing the HSV color channel , 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 is proposed, and the mathematical model of the depth of field information about image saturation and brightness is established, and the depth of field information is solved by the method of supervised learning , 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 of the whole image may easily lead to insufficient defogging in local areas.

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