Method for image processing based on matrix variable variation self-encoder

An image processing and autoencoder technology, applied in the field of computer vision and machine learning, which can solve problems such as destroying the spatial structure

Active Publication Date: 2018-12-04
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method for image processing based on a matrix variable variational autoencoder (Matrix-variate Variat...

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Embodiment Construction

[0009] The invention provides a method for image processing based on a matrix variable variational autoencoder (Matrix-variate VariationalAutoencoder, MVVAE),

[0010] Suppose there are N independent and identically distributed image sets Each image is represented as That is, the size of the input sample is a two-dimensional matrix of I×J. The present invention aims to model the statistical distribution logp of a set of images θ (X), and then effectively perform image reconstruction, denoising and completion. The image modeling process is based on the MVVAE network proposed by the present invention, so the core is to model the network and train to obtain the parameters of the network model.

[0011] To achieve the above object, the present invention adopts the following technical solutions:

[0012] 1. Definition of MVVAE model for image set distribution modeling

[0013] MVVAE model definition, such as figure 1 as shown,

[0014] in this model Is the input layer ma...

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Abstract

The invention discloses a method for image processing based on a matrix variable variation self-encoder so that a problem of damaging the spatial structure by image vectorization processing can be solved and thus image reconstruction, denoising and completion can be realized. Different from the traditional VAE, the method has the following advantages: the input, hidden layer features, latent variable distribution parameters of the model are described by using an inherent expression form -2D matrix of the image; with the definition of matrix Gaussian distribution and related properties, an explicit expression of an objective function of a novel model is deduced and then model parameters are calculated by using a stochastic gradient descent algorithm. In the model, modeling of the spatial structure and statistical information of the image data is carried out well because the involved modeling process is oriented at the matrix variable, so that the image reconstruction quality is enhanced, noises are removed, and the image completion is realized.

Description

technical field [0001] The invention belongs to the field of computer vision and machine learning, and in particular relates to an image processing method based on a matrix variable variational autoencoder. Background technique [0002] Image reconstruction, denoising and completion are important contents of image processing. Variational Autoencoder (VAE) is widely used in image processing related fields because it can well model the probability distribution of image data. VAE usually consists of an inference model (encoder) and a generation model (decoder). The objective function of the model includes two items: one is the reconstruction error of the image, which is generally measured by mean square error or cross entropy; the other is The Kullback-Leibler (KL) divergence is used to measure the similarity between the posterior of the feature latent variables learned based on the inference model and the prior distribution of the feature assumptions, which is equivalent to a ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T9/00
CPCG06T5/002G06T5/005G06T9/00G06T2207/10004
Inventor 李敬华闫会霞孔德慧王立春尹宝才
Owner BEIJING UNIV OF TECH
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