用CNN将数码照片转为柯达胶片风格，效果逼真但颗粒和光晕不足。
CNNs for Style Transfer of Digital to Film Photography
- 用简单CNN模型学习柯达Cinestill800T胶片风格
- MSE+VGG损失组合在色彩还原上表现最佳
- 公开配对数据集，适合风格迁移研究者
深度学习在风格化效果生成中应用日益广泛。本文使用简单的卷积神经网络，基于数码输入模拟柯达Cinestill800T胶片风格。我们测试了不同损失函数、输入噪声通道以及训练时随机缩放图像块的影响。结果表明，MSE+VGG损失组合在色彩还原上表现最优，可生成部分颗粒效果，但质量不高，且未产生光晕。本文贡献了一个由数码相机与胶片相机同步拍摄的配对图像数据集，供后续研究使用。
原文摘要 · Abstract (English)
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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