Abstract: The semantic content of an image in different styles has been difficult image processing task. A major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic in formation and thus, allow separating image content from style. Image representations derived from Convolutional Neural Networks optimized for object recognition, which make high-level image information explicit. Introduce a Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows producing new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well-known artworks. Results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high-level image synthesis manipulation.

Keywords: Image Reconstruction, Neural Network, Image representation, Visualization, Convolutional Neural Networks, Image Style Transfer, Starry Night Style, Waves Style, La Muse Style, Neural Style Transfer

PDF | DOI: 10.17148/IARJSET.2020.7822

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