Denoising Archival Films using a Learned Bayesian Model

@article{Moldovan2006DenoisingAF,
  title={Denoising Archival Films using a Learned Bayesian Model},
  author={Teodor Mihai Moldovan and Stefan Roth and Michael J. Black},
  journal={2006 International Conference on Image Processing},
  year={2006},
  pages={2641-2644}
}
We develop a Bayesian model of digitized archival films and use this for denoising, or more specifically de-graining, individual frames. In contrast to previous approaches our model uses a learned spatial prior and a unique likelihood term that models the physics that generates the image grain. The spatial prior is represented by a high-order Markov random field based on the recently proposed field-of-experts framework. We propose a new model of the image grain in archival films based on an… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 12 CITATIONS

Movie Denoising by Average of Warped Lines

  • IEEE Transactions on Image Processing
  • 2007
VIEW 8 EXCERPTS
CITES METHODS, BACKGROUND & RESULTS
HIGHLY INFLUENCED

Benchmarking Denoising Algorithms with Real Photographs

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017

Advanced Film Grain Noise Extraction and Synthesis for High-Definition Video Coding

  • IEEE Transactions on Circuits and Systems for Video Technology
  • 2009
VIEW 1 EXCERPT
CITES METHODS

Patch-Based Video Processing: A Variational Bayesian Approach

  • IEEE Transactions on Circuits and Systems for Video Technology
  • 2009

Fields of Experts

  • International Journal of Computer Vision
  • 2008
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-5 OF 5 REFERENCES