Method and system for removing fog effect from image rapidly, terminal and storage medium

Abstract

The invention discloses a method and system for removing the fog effect from an image rapidly, a terminal and a storage medium, the method comprise the steps of: optimizing a dark channel image with maximum filtering and guiding filtering after a sky area and a non-sky area are divided by OTSU, and determining a first transmittance according to the optimized dark channel image; adjusting the firsttransmittance by adopting an adaptive parameter adjusting method to obtain a second transmittance; according to the first transmittance and the second transmittance, removing the fog effect from thesky area and the non-sky area according to an atmospheric scattering model to obtain a fog effect removed image; adjusting the hue of the fog effect removed image by CLAHE. The system comprises an acquisition module, a division module, an optimization module, an adjustment module, a fog effect removing module and a hue adjusting module. According to the method and the system for removing the fog effect from image rapidly, the terminal and the storage medium, fog effect removing process of the image having the fog effect can be realized effectively, the defogging effect is good, and the processing operation efficiency is high. The method can be applied to the field of image processing.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method, system, terminal and storage medium for fast image defogging combined with sky segmentation and local transmittance optimization. Background technique [0002] Explanation of technical terms: [0003] OTSU: Otsu Algorithm. [0004] CLAHE: Contrast Limited Adaptive Histogram Equalization, limited contrast adaptive histogram equalization method. [0005] For photos taken in foggy weather, due to the existence of suspended particles in the air, the light will be scattered and the light reflected by the scene will be attenuated. Not only the quality degradation, image contrast and sharpness decrease, but also the difficulty of subsequent image processing and analysis, which directly affects the effectiveness of the outdoor visual acquisition system. Therefore, fast and effective dehazing and image restoration technology has important application value. ...

AI Technical Summary

Problems solved by technology

[0006] In recent years, many defogging algorithms have been proposed at home and abroad for the problem of image clarity in foggy days, which are mainly divided into two categories: one is based on image enhancement methods, which mainly enhance the contrast of degraded images, and highlight the features and characteristics of objects in the image. Valuable information, such as histogram equalization (Histogram Equalization, HE), Retinex algorithm, etc. However, image enhancement essentially only enhances the clarity and contrast of the image, and does not actually achieve the dehazing of the image The other is an image restoration method based on a physical model. By analyzing the law of atmospheric scattering and establishing an atmospheric scattering model, the physical process of image degradation is reversed to complete the optimal estimation of parameters and compensate for the lost information in the process of image degradation. , the image restored by this method is more natural, and the image quality has been improved

However, the currently commonly used image restoration method based on physical model - the global image restoration method based on dark channel prior theory, has many shortcomings, for example: 1. Due to the transmittance in the image local area Ω(x) The t(x) value is not constant, and the transmittance of the sky area is small, the overall is relatively smooth and the value difference between adjacent pixels is not large, which does not conform to the prior law of the dark channel, but this method uses The method of global restoration and setting the transmittance t(x) in the local area of ​​the image Ω(x) is constant, so the image restored by this method has serious problems such as blockiness and distortion of the sky area;2 , It uses soft matting to optimize the transmittance, but the soft matting algorithm has high computational complexity and long running time, which leads to the problem of low processing efficiency in this image restoration method, which is far from meeting the needs of real-time processing

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