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Image defogging system based on deep learning and an a priori constraint

A deep learning and image technology, applied in the field of image processing, can solve problems such as image defogging system errors, and achieve the effect of easy implementation, simple implementation, and remarkable defogging effect

Active Publication Date: 2018-07-10
NORTHEASTERN UNIV
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Problems solved by technology

Because the estimation process of atmospheric transmittance and atmospheric light often brings more errors to the image defogging system

Method used

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  • Image defogging system based on deep learning and an a priori constraint
  • Image defogging system based on deep learning and an a priori constraint
  • Image defogging system based on deep learning and an a priori constraint

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Embodiment Construction

[0057] The present invention will be further elaborated below in conjunction with the accompanying drawings of the description.

[0058] Such as figure 1 As shown, the present invention is a kind of image defogging system based on deep learning and prior constraints, comprising the following steps:

[0059] 1) By observing and comparing the image samples composed of the fog-free image and the synthesized fog image and counting the mean square error, a new image defogging prior constraint is proposed;

[0060] 2) Use the atmospheric scattering model to synthesize the image sample set in HDF5 data format required for training the model;

[0061] 3) For image defogging, guided by prior constraints, design an end-to-end multi-scale deep convolutional network, and use image sample sets, combined with multi-scale distance loss functions to optimize the training process of the model;

[0062] 4) Use the deep convolutional network model obtained by the above training to realize the ...

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Abstract

The invention relates to an image defogging system based on deep learning and an a priori constraint. The image defogging system comprises the steps of 1) performing visual observation comparison andmean square error statistics on image samples formed by a fog-free image and a synthetic atomized image, and proposing a new image defogging a priori constraint; 2) synthesizing an image sample set ofan HDF5 data format required for a training model by an atmospheric scattering model; 3) for the image defogging, taking the a priori constraint as the guidance, designing an end-to-end multi-scale deep convolutional network, and using the image sample set to optimize the training process of the model by combining with a multi-scale distance loss function; and 4) using the multi-scale deep convolutional network model obtained by the above training to realize the defogging operation of the real atomized image. The invention proposes a simple and effective a priori constraint, the visual contrast of the atomized image can be recovered through the multi-scale deep convolutional network model, the image texture is enhanced and the image defogging function is achieved.

Description

technical field [0001] The invention relates to an image processing technology, in particular to an image defogging system based on deep learning and prior constraints. Background technique [0002] In daily life, due to the influence of a large number of suspended particles in the atmosphere, part of the atmospheric light is absorbed or scattered, resulting in smog weather. Usually fog and haze will make the captured image appear blurred, low saturation or even image distortion. Due to the frequent occurrence of fog and haze, the implementation effect of image recognition, image detection and image tracking functions of smart devices has been greatly affected. Therefore, image defogging technology, as a challenging ill-posed problem, has become a research hotspot in the field of image processing. [0003] In the field of image defogging research, it is mainly divided into three categories according to technical characteristics: defogging methods based on auxiliary informa...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/90G06N3/04
CPCG06T7/90G06T2207/20081G06T2207/20084G06T2207/30192G06N3/045G06T5/73
Inventor 王安娜王文慧
Owner NORTHEASTERN UNIV
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