Handling Outliers in Non-blind Image Deconvolution

Sunghyun Cho1 Jue Wang2 Seungyong Lee1,2
1POSTECH                2Adobe Systems

In Proc. of IEEE International Conference on Computer Vision (ICCV) 2011
(Oral presentation)


Levin et al. 2007

Our method


Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.

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  author = {Sunghyun Cho and Jue Wang and Seungyong Lee},
  title = {Handling Outliers in Non-blind Image Deconvolution},
  booktitle = {Proc.\ IEEE International Conference on Computer Vision (ICCV 2011)},
  year = {2011},
  pages = {1--8}