Generation method and device for high-resolution ceramic flower paper pattern

Abstract

The invention belongs to the fields of information processing and artificial intelligence, and discloses a generation method and device for a high-resolution ceramic flower paper pattern. The method comprises the following steps that: (1) constructing a ceramic flower paper pattern sematic sketch generation model of artificial neural network approximate modeling; (2) according to the annotation data of the ceramic flower paper pattern, through a training model based on the artificial intelligence, obtaining the ceramic flower paper pattern sematic sketch generation model of a sematic sketch which has the consistent ceramic flower paper pattern with user intentions; (3) constructing the ceramic flower paper pattern sematic sketch generation model of the artificial neural network approximatemodeling; and (4) according to the annotation data of the ceramic flower paper pattern and the sematic sketch of the ceramic flower paper pattern, through the training method based on the artificialintelligence, obtaining the ceramic flower paper pattern generation model of the ceramic flower paper pattern with the high resolution. By use of the method disclosed by the invention, the generationprocess of the high-resolution ceramic flower paper pattern which has the consistent ceramic flower paper pattern with user intentions is divided into two stages, firstly, the ceramic flower paper pattern sematic sketch which has the consistent ceramic flower paper pattern with user intentions is generated according to image semantics contained in the user intentions, and then, the high-resolutionceramic flower paper pattern which has the consistent ceramic flower paper pattern with user intentions is generated according to the ceramic flower paper pattern sematic sketch.

Description

technical field [0001] The invention belongs to the fields of information processing technology and artificial intelligence, and in particular relates to a method and device for generating high-resolution ceramic decal patterns. Background technique [0002] Since the invention of the Generative Adversarial Network in 2014, a large number of scientific research results have appeared in the field of scientific research. Although people can generate images in various formats according to the needs of users, the resolution of the generated images is relatively low, such as 64*64, 128*128, the current maximum resolution usually selected is 256*256, for example, the patent application CN107016406A discloses a four-layer generation network using a three-layer deconvolution network layer plus a fully connected layer to achieve a pixel distribution rate of 256*256 The program for generating images of diseases and insect pests, and the patent application CN107220600A discloses using ...

AI Technical Summary

Problems solved by technology

[0004] Approximate real pictures are only distributed as a fairly compact manifold (Manifold) in the high-dimensional image space. The training process of the generative confrontation network is to enable the artificial neural network to sample data from this popular in the vast high-dimensional image space. Therefore, when the resolution of the generated image is larger, the dimension of the image space is higher. Although the artificial neural network can sample the image data in the image space, it is difficult to sample the image data in this fashion, which makes the high-resolution image generated by the current technology Pixel resolved images are blurry and unrealistic images

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