Convolutional neural network image restoration method for reflective metal visual detection

A convolutional neural network and visual inspection technology, applied in neural learning methods, biological neural network models, image enhancement, etc., can solve problems such as poor application effect of image restoration technology, improve image restoration quality, and save cumbersome links Effect

Active Publication Date: 2019-08-06
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

It can be seen that the image restoration technology based on the linear degradation model is not effective in the application of...

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  • Convolutional neural network image restoration method for reflective metal visual detection
  • Convolutional neural network image restoration method for reflective metal visual detection
  • Convolutional neural network image restoration method for reflective metal visual detection

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Embodiment Construction

[0014] The present invention will be further described in detail below in conjunction with the embodiments and accompanying drawings.

[0015] The present invention is a convolutional neural network image restoration method for reflective metal visual detection, such as figure 1 As shown, the method includes the following steps:

[0016] Step 10, introducing slack variables to decouple the maximum a posteriori probability image restoration model;

[0017] Maximum Posteriori Probability Image Restoration Model argmax x p(x|k,y)∝p(yk,x)p(x) by introducing slack variables Likelihood with the prior term Where y, k, and x represent blurred image, blurred kernel, and clear image respectively, and the constraints are

[0018] Step 20, constructing the principal component of the logarithmic likelihood item based on the Poisson distribution, introducing a nonlinear degradation model, and eliminating pixels saturated with reflective metal;

[0019] Construct log-likelihood te...

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Abstract

The invention provides a convolutional neural network image restoration method for reflective metal visual detection, and the method comprises the steps: introducing a relaxation variable, and decoupling a maximum posteriori probability image restoration model; constructing a logarithmic likelihood item principal component based on Poisson distribution, introducing a nonlinear degradation model, and removing reflective metal saturated pixels; constructing a logarithm prior item principal component based on the convolutional neural network, and constraining an image restoration solution space;and alternately updating the likelihood item and the prior item, and optimizing the final restored image through multi-stage connection. According to the method, the advantages of the convolutional neural network are utilized, the tedious link of manual design of priori items is omitted, the image restoration quality is improved based on the nonlinear degradation model, and the application of theimage restoration technology in reflective metal visual detection is facilitated.

Description

technical field [0001] The invention relates to the field of image restoration, in particular to a convolutional neural network image restoration method for reflective metal visual detection. Background technique [0002] Visual inspection technology has been widely used due to its high accuracy, non-contact and good applicability. Visual inspection under dynamic imaging conditions is prone to motion blur, which reduces the reliability of inspection results. Image restoration technology is an important auxiliary tool for visual inspection technology to obtain potential clear images from observed blurred images. There are many kinds of visual inspection targets, among which metal targets are difficult to avoid reflection phenomenon, especially under dynamic imaging conditions. Reflections cause oversaturated areas of the image, known as saturated pixels. Saturated pixels do not conform to the assumptions of image linear degradation models, and image restoration techniques ...

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Application Information

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IPC IPC(8): G06T5/00G06N3/02G06N3/08
CPCG06T5/005G06T2207/20084G06T2207/20081
Inventor 刘桂雄王博帝
Owner SOUTH CHINA UNIV OF TECH
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