The Visual Computer

, Volume 30, Issue 6–8, pp 661–671 | Cite as

Fast high-quality non-blind deconvolution using sparse adaptive priors

Original Article

Abstract

We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.

Keywords

Non-blind deconvolution Adaptive priors Deblurring Computational photography 

Notes

Acknowledgments

This work was sponsored by CNPq (Grants 482271/2012-4 and 308936/2010-8). We thank the authors of the compared techniques for making their code available, and Shan et al. for providing the photographs and kernels shown in Figs. 9 and10.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Uniritter, Laureate International UniversitiesPorto AlegreBrazil
  2. 2.Instituto de Informática, UFRGSPorto AlegreBrazil

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