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Image Dehazing Method Based on Multi-scale Dark Channel Prior Cascade Deep Neural Network

A deep neural network and dark channel prior technology, applied in the field of image dehazing based on multi-scale dark channel prior cascaded deep neural network, can solve the problems of strong data dependence and lack of real scene contrast.

Active Publication Date: 2020-06-12
ROCKET FORCE UNIV OF ENG
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Problems solved by technology

However, this method is very dependent on data due to the lack of comparison of real scenes.

Method used

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  • Image Dehazing Method Based on Multi-scale Dark Channel Prior Cascade Deep Neural Network
  • Image Dehazing Method Based on Multi-scale Dark Channel Prior Cascade Deep Neural Network

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Embodiment Construction

[0044] like figure 1 As shown, the image defogging method based on the multi-scale dark channel prior cascaded deep neural network of the present invention comprises the following steps:

[0045] Step 1. Establish a fog image training set: use the image data set of known depth to synthesize a group of foggy image training sets according to the atmospheric scattering model, effectively expanding the image data volume of the foggy image training set;

[0046] In this embodiment, the image data set of known depth includes the NYU image data set, and the public standard data set is used to train the experimental results. The method has strong adaptability, high image processing precision, and good defogging effect.

[0047] Step 2: Dehazing a single random hazy image, the process is as follows:

[0048] Step 201. Randomly extract a foggy image from the foggy image training set in step 1, and normalize the image size of a single random foggy image to obtain an image size of 2 m ×...

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Abstract

The invention discloses an image defogging method based on a multi-scale dark channel prior cascade deep neural network. The method comprises the following steps: 1, establishing an atomized image training set; 2, defogging a single random foggy image; 3, calculating a loss objective function of the original single foggy image; 4, updating the weight parameter set; 5, calling a new single random foggy image, circulating the step 2 to the step 4 until the loss target function of the original single foggy image is smaller than the loss target function threshold, and determining a final cascade defogging model; and 6, defogging a single actual foggy image. According to the invention, the convolutional neural network is used to estimate dark channel and global illumination parameters on imagesof different scales. The deep neural network is used as a model, and then the dark channel and the defogged image are fused step by step. Finally, the defogged image is obtained through supervised learning. The feature modeling capability of the deep neural network is effectively utilized. The parameter fusion of different scales is achieved. The high-resolution defogged image can be obtained under the condition of few model parameters.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image defogging method based on a multi-scale dark channel prior cascading deep neural network. Background technique [0002] Images collected under severe weather such as fog and haze will have quality degradation due to atmospheric scattering, which will make the image color off-white, reduce the contrast, and make it difficult to identify object features. It can also lead to biased understanding of image content. Image defogging refers to the use of specific methods and means to reduce or even eliminate the adverse effects of suspended particles in the air on images. Single image defogging refers to dehazing to obtain a clear image under the condition of only one foggy image. [0003] At present, single image dehazing methods are mainly divided into three categories: the first category is based on image enhancement methods, the second category is base...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/003G06T5/007G06T2207/10004G06T2207/20024G06T2207/20081G06T2207/20084
Inventor 崔智高苏延召李爱华王涛姜柯蔡艳平冯国彦李庆辉
Owner ROCKET FORCE UNIV OF ENG
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