Image style migration method combining meta-learning mechanism and feature fusion

Abstract

The invention discloses an image style migration method combining a meta-learning mechanism and feature fusion. According to the image style migration method, feature fusion based on convolution calculation and a method for decoding a feature map by using the meta-learning mechanism are combined. Firstly, content features and style features are preliminarily fused through convolution calculation,weighted summation operation is carried out on a preliminarily fused feature map and a preliminarily fused content feature map, and the stylization degree is controlled by adjusting the weight; and then the fused feature map is decoded into a stylized image by using a meta-learning mechanism, and secondary learning is carried out on the style in the decoding process, so that full expression of style features is ensured. According to the invention, the quality of the stylized image is improved, so that the style of the synthesized image is fairer than the original style; controlling the degreeof stylization based on the characteristics of the content image and the style image; a meta-learning mechanism is used to simultaneously carry out style secondary learning and feature map decoding operation so that style migration time is shortened and stylization of any image is rapidly realized.

Description

technical field [0001] The invention relates to an image fusion and image reconstruction method, in particular to an image style transfer method based on meta-learning and feature fusion, which belongs to the field of deep learning. Background technique [0002] The predecessor of image style transfer is image texture synthesis. Since the rise of deep learning in 2012, researchers have used deep learning methods for image texture synthesis. In 2015, Gatys et al. proposed to use convolutional network to extract image texture features, thus resulting in the field of image style transfer. Image style transfer refers to transferring the style of the style image to the content image to obtain a stylized image, and its essence is the fusion of images. At present, the application of image style transfer technology is hot, not only in image processing, but also in the field of traditional manual operations such as manga manuscript coloring. [0003] Image style transfer technolog...

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Problems solved by technology

[0007] Purpose of the invention: Aiming at the problem that existing arbitrary style transfer methods cannot achieve high-quality fast stylization, the present invention provides an image style transfer method that combines meta-learning mechanism and feature fusion, fully expresses style features, and coordinates local and global style modes While reducing the time for image compositing

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