The use of deep learning in stylistic effect generation has seen increasing use over recent years. In this work, we use simple convolutional neural networks to model Cinestill800T film given a digital input. We test the effect of different loss functions, the addition of an input noise channel and the use of random scales of patches during training. We find that a combination of MSE/VGG loss gives the best colour production and that some grain can be produced, but it is not of a high quality, and no halation is produced. We contribute our dataset of aligned paired images taken with a film and digital camera for further work.
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