Image defogging method based on convolutional neural network

Abstract

The invention discloses an image defogging method based on a convolutional neural network. The method comprises the following steps: transforming an atmospheric scattering model; building an Encoder-decoder network, and estimating an intermediate transmission graph; processing an image restoration problem; and establishing a Dehazer network to realize a Dehazer function, and outputting a defoggedimage. The Encoder-decoder network does not need to change the network structure and related parameters, the influence of noise and jitter can be reduced, important features related to a target imageare obtained, and an accurate intermediate transmission graph is output. The Dehazer network is simple in structure, convenient to train, capable of sharing multiple parameters, appropriate in calculation overhead and stable in network performance, gradient disappearance and explosion can be effectively prevented, and defogged images can be conveniently and rapidly output. According to the methodprovided by the invention, the defogged image can be efficiently and quickly output, the performance of the established network is relatively stable, the influence of fog or haze can be well eliminated, the defogging quality of the image is effectively improved, and the defogging effect is relatively ideal.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image defogging method based on a convolutional neural network. Background technique [0002] Image is the visual basis for human beings to obtain information from the real world, an important medium for transmitting information, and an important means of expressing information. Image processing technology emerges as the times require, and it plays an important role in many fields such as medical treatment, transportation, archaeology, agriculture, industry, and construction. Image processing is a technology that uses computers to perform correlation analysis and processing on images, and its purpose is to process images into desired results. Image processing technology generally includes methods such as image compression, image enhancement, image restoration, and image recognition. The image defogging method proposed by the present invention belongs to image restora...

AI Technical Summary

Problems solved by technology

Although the single image dehazing method based on multi-scale convolutional neural network (Single Image Dehazing via Multi-Scale Convolutional Neural Networks, MSCNN) performs better than the traditional method, it performs well in bright white areas such as the sky, and its performance is in the deep area. also decreased, but it may be because the estimation of the haze level contained by these methods is lower than its real amount, resulting in incomplete dehazing, and the output clean image still contains some haze that has not been cleaned, and there are after-fog The image color of the image is dark, and it is impossible to obtain an ideal defogging result.

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