Image conversion method based on variation automatic encoder and generative adversarial network

An automatic encoder and encoder technology, applied in the field of image conversion, can solve problems such as difficulty in obtaining image pairs and inconvenient conversion

Inactive Publication Date: 2017-09-01
SHENZHEN WEITESHI TECH
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Problems solved by technology

However, most of the existing image-to-image translation methods are based on supervised learning, requiring a training dataset consisting of two co

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  • Image conversion method based on variation automatic encoder and generative adversarial network
  • Image conversion method based on variation automatic encoder and generative adversarial network
  • Image conversion method based on variation automatic encoder and generative adversarial network

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Embodiment Construction

[0031] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0032] figure 1 It is a system framework diagram of an image conversion method based on a variational autoencoder and a generated confrontation network in the present invention. It mainly includes variational autoencoder (VAE), weight sharing, generative confrontation network (GAN), and learning.

[0033] Variational Autoencoder (VAE), Encoder-Generator pair {E 1 , G 1} constitutes the VAE 1 the x 1 VAE of the domain; for an input image x 1 ∈ x 1 , VAE 1 First pass the encoder E 1 map to latent space The code in is then decoded by the generator G 1 Reconstruct the input image; the encoder outputs the average vector and variance vector where the latent code z 1 distribut...

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Abstract

The invention provides an image conversion method based on a variation automatic encoder and the generative adversarial network. The method is mainly characterized by comprising the variation automatic encoder (VAE), weight sharing, generating the generative adversarial network (GAN) and learning, in the process, a non-monitored image is utilized to learn a bidirectional conversion function between two image domains in an image conversion network framework (UNIT), VAE and VAE are comprised, modeling for each image domain is carried out through utilizing the VAE and the VAE, mutual action of an adversarial training target and a weight sharing constraint is carried out, corresponding images are generated in the two image domains, the conversion image is associated with an input image of each domain, and image reconstruction flow and image conversion flow problems can be solved through training network combination. The method is advantaged in that the non-monitoring image is utilized to the image conversion framework, images in the two domains having not any relations are made to accomplish conversion, a corresponding training data set formed by the images is not needed, efficiency and practicality are improved, and the method can be developed to non-monitoring language conversion.

Description

technical field [0001] The invention relates to the field of image conversion, in particular to an image conversion method based on a variational automatic encoder and a generation confrontation network. Background technique [0002] With the emergence of photographic technology, the popularization of television and movies, advertisements, newspapers, books, the Internet and many other image-based social media are developing rapidly, image-related technologies are becoming more and more important. Mapping images from one domain to another (image-to-image translation) has a wide range of applications. For example, the marine environment has poor visibility and often has haze, etc. Predicting the relevant conditions of the environment through image conversion will facilitate maritime traffic management, fishing and other marine operations; realize the conversion of images from day to night, from sunny to rainy Transformation, etc., so that people can predict the environment i...

Claims

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/007
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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