An image defogging method based on blue channel correction

Abstract

The invention discloses an image defogging method based on blue channel correction, which is applied in the technical field of computer vision and image processing, and aims at the existing histogram equalization algorithm, multi-scale Retinex (MSR) algorithm and dark channel prior defogging method and other algorithms, the problem of poor defogging effect and large algorithm limitations; the present invention firstly extracts the information of the red, green and blue channels, and combines them to obtain the depth and transmittance information, and then converts the image red, The green and blue channels are calculated separately to obtain the defogged image of each channel, and the final combined image is obtained to obtain the final defogged image; it has fewer calculation parameters, simple and fast calculation, good effect, and no color cast, which is very suitable for outdoor fog images Advantages of processing.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image processing, in particular to an image defogging technology. Background technique [0002] Images taken under better natural conditions often have higher contrast and saturation. In foggy weather, natural light is scattered by atmospheric particles, resulting in reduced image contrast, brightness, and saturation. For example, the same lawn will appear green with high saturation when the weather is good (clear and fog-free), but it will appear green with low saturation in foggy weather, and the overall image is gray and the details are blurred. The images obtained in this way will cause great interference during image analysis, so it is extremely important to correct the images obtained under foggy weather. [0003] Representative dehazing algorithms include histogram equalization algorithm, multi-scale Retinex (MSR) algorithm and dark channel prior dehazing algorithm, etc. Amon...

AI Technical Summary

Problems solved by technology

This method is not very effective in the sky area with high brightness, and the sky area is prone to color cast

In addition, in recent years, more and more deep learning-based dehazing algorithms have achieved good results, but they require a large number of images with real reference data for model training, but foggy images generally cannot be trained accordingly Sample images, making the limitations of this type of method relatively large

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