Abstract
A novel application of the transfer of style via deep convolutional neural networks (CNN) is presented, utilising an ensemble of paintings, by Vincent van Gogh to stylise Martian landscape imagery. Neural style transfer (NST)[1][2] can be used to create artistic work through rendering a content image in the form of a style image. Through applying NST to Martian landscape imagery, a collection of artwork is presented that emulates what van Gogh may have painted had the artist visited Mars.
Introduction
Since the dawn of humanity celestial phenomena have inspired art, science and exploration. Throughout history these celestial phenomena have been viewed beneath the terrestrial sky. In the 21st century, through technological advances in spacecraft reusability and artificial intelligence, new celestial phenomena may be viewed by astronauts beneath extra-terrestrial skies for the first time.[3]
The Dutch post-impressionist painter Vincent van Gogh (1853-1890) is widely regarded as one of the most influential artists in Western art. Many of van Gogh’s most famous artwork includes celestial objects, such as in stars in The Starry Night (1889)[4] and The Starry Night over the Rhône (1888)[5] and the Moon in Landscape with Wheat Sheaves and Rising Moon, (1889)[6]. Here a collection of work highlighting the achievements of the planetary science community in exploring the Martian environment is presented. This collection imagines what Vincent van Gogh would have painted had the artist have visited the Red Planet.
Methods
NST was developed by[1] and can be used to create novel artistic work by rendering one image in the style of another. NST uses a previously trained CNN called the VGG-19 network; a 19 layer neural network that has been trained on a large dataset of ImageNet[7] images. Training on this large dataset of images allows the VGG-19 neural network to recognise low and high-level features in images.
A key aspect of NST involves defining and minimising the content and style cost functions. Once these functions are defined, they are added together to create a total cost function. Using the Adam[8] optimisation algorithm, the aforementioned total cost function, which makes the generated image follow the content of the content image and the style of the style image simultaneously, which after several iterations creates stylised images. For further details the author refers the reader to the literature[1][2].
In this research 30 paintings by Vincent van Gogh were identified that included the Sun, Moon and stars, these artworks contribute to the collection of style images used with NST. Similarly, 30 famous images of Martian landscape imagery were selected, including images from NASA’s Spirit, Opportunity and Curiosity Rovers and Viking 1/2 landers, these artworks contribute to the collection of content images used with NST. The author found that creating a montage of a particular feature, i.e. the sky or cobbled streets, followed by applying NST from [2] with a linear colour transfer provided aesthetically better results than utilising semantic style transfer with guided gram matrices and masks.
Results
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)
Figure 1: (Left) The content image of a Martian eclipse, Phobos transits the Sun at sunset, NASA, 2010 [10]. (Right) The result: A Martian eclipse, Phobos transits the Sun at sunset, captures the beauty of nature in the solar system, showing a blue sunset eclipse of the Sun by the Martian moon Phobos. This work has been rendered in the style of Vincent van Gogh’s, 1888, The Cafe Terrace on the Place du Forum, Arles, at Night.[9]
Conclusions
This research has described a novel application of NST[1][ to Martian landscape imagery, through presenting a collection of Martian van Gogh artworks, showing that deep convolutional neural networks, offer a new way of visualising the Martian environment.
Acknowledgements
The author would like to thank the UK Space Agency for their support through the Aurora Science Studentship, STFC:535385. This work is freely available to download from Oxia Palus at: https://www.oxia-palus.com/.
Compute Environment
Instance: GCP Compute Engine. Image: c3-deeplearning-tf-1-14-cu100-20190724. Hardware: NVIDIA 16GB V100, 8CPU GCP Deep Learning VM with CUDA enabled. Software Tools: Tensorflow 1.14, CUDA-10.0, scipy, numpy, matplotlib, PIL. Training Protocols: Style transfer run for 10000 iterations, with an RGB style image. Content cost coefficient alpha=10 and style cost coefficient beta=40. For the style layers conv1_1=0.2, conv2_1=0.2, conv3_1=0.2, conv4_1=0.2 and conv5_1=0.2. Layer conv4_2=1, used as the content layer. Adam optimizer utilized with learning rate 1.0. Colour channels=3. Noise image ratio=0.6.
References