mlsanityData to 7 Oct 2026

CNNs for Style Transfer of Digital to Film Photography

arXiv 2411.15967 ↗First posted 24 Nov 2024cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 24 Nov 2024.

Builds on

21 of 29 references.

References · 21

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  7. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  8. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  9. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  10. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  11. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  12. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
9 more
  1. 2018Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transformcited by 369
  2. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  3. 2017Deep Photo Style Transfercited by 168
  4. 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
  5. 2017DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networkscited by 156
  6. 2018The Contextual Loss for Image Transformation with Non-Aligned Datacited by 135
  7. 2019Zoom To Learn, Learn To Zoomcited by 114
  8. 2016Deep Colorizationcited by 70
  9. 2021Semi-Supervised Raw-to-Raw Mappingcited by 16

Built on by

Not cited yet.