Deep Learning Technique for Transfer of Artistic Style to Images and Videos

V. V. Rama Prasad*, G. Vineela Ratna**
*-** Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.
Periodicity:April - June'2020
DOI : https://doi.org/10.26634/jip.7.2.17555

Abstract

By composing a complex interplay between the content and style of an image, humanity has mastered the ability to create unique visual experience in fine art, particularly painting. Transfer of the artistic style is a problem in which image style is transformed into image content and generates image stylization. Style transformation can be applied over the entire video sequence by adding image style to video. We use perceptual loss functions to train feed-forward neural network and extract high-level features from the trained networks. We show the effects of image style transfer and video style transfer, through training feed forward network. Our network delivers faster results when compared with Gatys proposed optimization-based method. Resnet is added to the network as an improvisation to transformation network. Pruned Resnet is used, and it gives high computation speed, less size of memory and good performance.

Keywords

Artistic Style Transfer, Deep Learning, Perceptual Loss Function, Transformation Network, Video Style Transfer.

How to Cite this Article?

Prasad, V. V. R., and Ratna, G. V. (2020). Deep Learning Technique for Transfer of Artistic Style to Images and Videos. i-manager's Journal on Image Processing, 7(2), 13-21. https://doi.org/10.26634/jip.7.2.17555

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