End-to-end image defogging method based on multi-feature fusion

Abstract

The invention discloses an end-to-end image defogging method based on multi-feature fusion. The end-to-end image defogging method comprises the steps of 1, acquiring a sample data set; 2, building an end-to-end image defogging network model based on multi-feature fusion, wherein the model comprises a basic network taking a global feature fusion attention module as a core, a prior feature extraction module supporting back propagation and a prior feature adaptive fusion module; the dark channel priori features and the color attenuation priori features enter a priori feature adaptive fusion module to be fused, and then are fused with deep learning features obtained by the basic network; step 3, constructing a loss function; 4, training an end-to-end image defogging network model based on multi-feature fusion; and step 5, carrying out defogging processing on a to-be-processed image by using the trained model to obtain a defogged image. Experimental results of a synthetic data set and a real data set show that the defogging capability and migration capability of the model in a real scene are improved, the parameter quantity is small, and rapid defogging can be realized.

Description

technical field [0001] The invention belongs to the technical field of foggy image processing, and relates to an image defogging method based on fusion of multiple features. Background technique [0002] With the advent of the information age, various intelligent vision systems are widely used in the fields of intelligent transportation, intelligent security and military reconnaissance. They use the image as the basic carrier to transmit information and perform intelligent processing and analysis on it, such as target detection, recognition and tracking, etc., but these advanced vision tasks have certain requirements on the quality of the image. In the haze weather, due to the absorption and scattering of the reflected light and atmospheric light by a large number of suspended particles in the air, the quality of the captured image is greatly reduced, and there are problems such as reduced contrast, color distortion and reduced clarity. It will seriously affect the applicat...

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Problems solved by technology

[0005] In view of the defects or deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide an image defogging method based on the fusion of prior features and depth features. problem, improving the defogging effect of the deep learning model in real scenes; and the model is relatively lightweight, which can achieve fast defogging

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