Neural Photo-Finishing

Ethan Tseng, Yuxuan Zhang, Lars Jebe, Xuaner Zhang, Zhihao Xia, Yifei Fan, Felix Heide, Jiawen Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Image processing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data; however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.

Original languageEnglish (US)
Article number3555526
JournalACM Transactions on Graphics
Issue number6
StatePublished - Nov 30 2022

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design


  • image processing
  • photo-finishing
  • raw processing


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