Abstract
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.
Abstract (translated)
近年来，深度学习在风格效果生成中的应用越来越广泛。在这项工作中，我们使用简单的卷积神经网络根据数字输入来模拟Cinestill800T胶片的效果。我们测试了不同损失函数的影响、添加输入噪声通道以及训练过程中随机缩放图像块的作用。我们发现，结合MSE/VGG损失可以产生最佳的颜色效果，并且能够生成一些颗粒感，但质量不高，也没有生成晕影效果。我们贡献了一组使用胶片相机和数字相机拍摄的对齐配对图像数据集，以供进一步研究。
URL
https://arxiv.org/abs/2411.15967
