Joint estimation image defogging method based on convolutional neural network

A convolutional neural network and neural network technology, applied in the field of single image defogging, can solve the problems of single applicable scene, cumbersome training of nonlinear fitting ability, etc., to achieve clear images, avoid incomplete defogging, and high efficiency Effect

Inactive Publication Date: 2018-11-13
XIDIAN UNIV
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Problems solved by technology

[0010] The purpose of the present invention is to propose a joint estimation image defogging method based on convolutional neural network to solve the problems of re

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  • Joint estimation image defogging method based on convolutional neural network
  • Joint estimation image defogging method based on convolutional neural network
  • Joint estimation image defogging method based on convolutional neural network

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Embodiment Construction

[0030] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0031] refer to figure 1 , the concrete realization of the present invention is as follows:

[0032] Step 1: Build a convolutional neural network architecture under the Caffe framework.

[0033] Such as figure 2 As shown, the neural network constructed by the present invention is composed of three parts, the feature sharing part, the fog map atmospheric light value estimation branch and the transmittance estimation branch, wherein:

[0034] The feature sharing part includes three convolutional units, each convolutional unit includes a convolutional layer and a normalization operation with a corresponding scaling factor γ n and offset coefficient α n , the weight W of the convolution operation in the convolutional layer n The size is 7*7, 5*5, 3*3, and the convolution step is 1;

[0035] The atmospheric light value estimation branch includes...

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Abstract

The invention discloses a single image defogging method, which mainly solves the problems of constrained nonlinear fitting ability, cumbersome training and single applicable scene in the prior art. The scheme comprises: constructing a convolutional neural network consisting of a feature sharing part, a haze atmospheric light value estimation branch and a transmittance estimation branch under the Caffe framework; acquiring a group of fog-free image set J, artificially fogging J to obtain a fog image set I; averagely dividing I and J into multiple paired image groups by batch size, and sequentially inputting in order for 200000 times to the neural network for training; inputting the image I that needs to be defogged into the trained neural network, and outputting the atmosphere light value Aand transmittance T; and calculating according to the atmosphere light value A and transmittance T to obtain a fog-free image J. The invention may well maintain the contrast and color saturation of the restored image, with the peak signal to noise ratio and the structural similarity both superior to the prior art, and may be used for the clear processing of the foggy image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for defogging a single image, which can be used for clearing processing of a single foggy image captured by an imaging system. Background technique [0002] Affected by severe weather such as smog and haze in real conditions, the image quality captured by imaging equipment is generally low, often with low contrast, color shift, and low information recognizability. These degraded images not only affect the subjective experience of the human eye, but also seriously affect the performance of various intelligent visual information processing systems. Therefore, the clear processing of foggy images has very important practical application value. [0003] At present, the image and video dehazing algorithm based on the atmospheric scattering model is a research hotspot, and the key issue is how to estimate the atmospheric light and transmittance. [0004]...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/003G06T2207/20081G06T2207/20084
Inventor 王柯俨赵熹王迪李云松许宁雷杰陈静怡
Owner XIDIAN UNIV
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