IMAGE AND VIDEO STYLE TRANSFER USING CONVOLUTIONAL NEURAL NETWORK

In this paper, the proposed image style transfer methodology using the Convolutional neural network, given a random pair of images, a universal image style transfer technique extract, the image texture from a reference image to synthesize an output supported the design of a content image. Image processing algorithms supported second-order statistics however are either computationally high-priced or vulnerable to generate artifacts because of the trade-off between image quality and run-time performance. Recently there has been much progress within the field of image style transfer, a process that aims at redrawing an image within the type of another image. During this paper, the proposed technique consists of a normalization step and a smoothing step. Whereas the stylization step transfers the design of the reference image to the content photograph, the smoothing step ensures spatially consistent stylizations. Every one of the steps includes a closed-form solution and maybe computed efficiently. This paper tends to conduct extensive experimental validations. The results show that the proposed technique generates photorealistic stylization outputs that are additional most popular by human subjects as compared to those by the competitive strategies, whereas, running a lot of faster.