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Dangelo, E., Alahi, A., Vandergheynst, P., et al. (2012) Beyond Bits: Reconstructing Images from Local Binary Descriptors. International Conference on Pattern Recognition, Tsukuba, Japan, 11-15 November 2012, 935-938.

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• TITLE: Research on Image Generation and Style Transfer Algorithm Based on Deep Learning

  AUTHORS: Ruikun Wang

  KEYWORDS: Deep Learning, Image Generation, Style Transfer

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.9 No.8, August 28, 2019

  ABSTRACT: Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution based on a deep learning framework to realize image generation and style transfer. The method first used the conditional generation to resist the network, optimizes the loss function of the training mapping relationship, and generated the actual image from the input sketch. Then, by defining and optimizing the perceptual loss function of the style transfer model, the style features are extracted from the image, thereby forming the actual The conversion between images and stylized art images. Experiments show that this method can greatly reduce the work of coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick figures into actual object images.