Frog day image rapid restoration method based on feedback mean value filtering

Abstract

The invention discloses a frog day image rapid restoration method based on feedback mean value filtering, for restoration processing of a frog day image. The method takes an atmosphere scattering model as a basis, comprises specific steps shown in an attached drawing 1 of the abstract of the descriptions, and mainly involves restoration of the frog day image through four steps including image preprocessing 2, atmosphere light value estimation 7, transmission graph estimation 12 and tone adjustment 14. The main technical features of the invention comprise weight graph establishing 4, mean value filtering 9, parameter repairing fuzzy edge feedback 10 and high-light area transmissivity self-adaptive correction 11. The advantages are as follows: atmosphere light values can be accurately estimated, influences exerted by a non-sky high-light area are avoided, at the same time, the complexity of the algorithm for estimating a transmission graph is low, and quite good restoration effect can be achieved within a short period. The method provided by the invention is applied to restoration processing of a degraded image under the condition of foggy weather, dusty weather and the like.

Description

Technical field: [0001] The invention relates to the technical fields of digital image processing and computer vision, in particular to a method for quickly restoring foggy images based on feedback mean value filtering. Background technique: [0002] In foggy conditions, the propagation of light will be affected by the suspension of water droplets in the air and scatter, and then deviate from the original propagation path. Distortion and contrast are significantly reduced, which seriously affects the analysis and identification of image content by human eyes or computer vision systems. [0003] At present, single image defogging techniques can be generally divided into image enhancement-based defogging methods and physical model-based image restoration methods. The currently developing method is the image restoration method based on the physical model, which can solve the haze-free image through the relevant prior information and the physical model. Among them, the represe...

AI Technical Summary

Problems solved by technology

Among them, the representative algorithm is a single image defogging algorithm based on the dark channel prior proposed by HE et al. This algorithm makes statistics on a large number of outdoor fog-free images, and finds a dark channel prior that can estimate the fog concentration. According to the experimental law, the scene transmittance reflecting the fog concentration can be obtained by using this law, and then combined with the physical model of fog imaging to solve the fog-free image, the algorithm has a good dehazing effect, and the restored image color is natural, but the space and time of the whole algorithm are complicated. The intensity is high, and when there are large bright areas such as the sky in the image, the dark channel prior will be invalid. At the same time, because a soft matting algorithm is involved in the initial transmittance optimization process, the time and space of the entire algorithm are complicated. High degree, difficult to meet the practical application

To sum up, although the HE algorithm has a good defogging effect, there are still four problems to be solved: (1) The time and space complexity of the algorithm is large, which is difficult to meet the practical application; (2) The restoration of large white areas such as the sky Distortion is prone to occur, which affects the restoration result; (3) the estimation of atmospheric light value is not accurate enough, and the result is easily affected by small bright areas

(4) The scope of application of the method is narrow, and it cannot be processed when the fog shows a color cast other than white

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