A Real-time Video Dehazing Method Using Grayscale Guided Filtering

Abstract

The invention discloses a real-time video defogging method using grayscale guided filtering, comprising: acquiring images from video streams; converting original images into grayscale images; conducting guided filtering on grayscale images; down-sampling the original images to obtain Small image; calculate the dark channel image of the small image according to the definition formula of the dark channel; use the quadtree method to find the dense fog area or sky area; use the improved atmospheric light scattering model to restore the image, and output the foggy video. The advantages of the present invention are: the restored image has a thorough defogging effect, bright and bright colors, and will not have color distortion in the sky area like the dark primary color theory defogging algorithm, and white edges will appear in places where the depth of field changes greatly. For small fog, Both medium fog and heavy fog have very good defogging effects, and the amount of calculation is small, and the processing speed is fast and stable.

Description

Technical field[0001]The present invention relates to the field of video processing, and more particularly to a real-time video denger method using graygraph guiding filter.Background technique[0002]Under smog weather conditions, due to the scattering of the rays in the atmosphere, it is necessary to reduce the normal operation of the outdoor image acquisition system due to the scattering of the rays in the atmosphere. Therefore, it is very meaningful to improve video quality for video captured under smog weather.[0003]In recent years, domestic and foreign scholars have mainly studied the degeneration of single images, and a large breakthrough in this field. At present, the method of foggy day image processing is mainly divided into two major categories: image recovery density algorithm based on image enhancement density algorithm and physical model. Image enhanced typical methods such as Retinex theory-based density algorithm, the class algorithm can effectively increase contrast, ...

AI Technical Summary

Problems solved by technology

Typical image enhancement methods such as the defogging algorithm based on Retinex theory can effectively increase contrast, highlight details, and can defog and improve the visual effect of foggy images to a certain extent. However, since the degradation of the image is not considered, The ability to restore image information is limited, which can easily lead to the loss of part of the image information, and the haze cannot be fundamentally removed, and the processed image is prone to grayscale, and the algorithm complexity is high, which cannot meet the real-time requirements

The other is a typical method of image restoration based on a physical model, such as the dark channel prior defogging algorithm. In the dark channel prior defogging algorithm, the atmospheric light in the atmospheric scattering model is assumed to be a constant value, and the transmittance is calculated through the dark channel prior theory. Figure to restore foggy images, for general outdoor foggy images can achieve better defogging effect, but the complexity of the algorithm is not as high as the requirements of real-time video processing, and the sky area of ​​the image after defogging is prone to oversaturation and color distortion , the overall image is too dark, etc.

Scholars at home and abroad have proposed many defogging algorithms, but most of them are only applicable to the defogging of a single image. At present, the research results of real-time video defogging are still relatively few, and most of the existing methods are in terms of computing speed and The defogging effect is still unsatisfactory, and the processing speed and defogging effect cannot be considered

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