Abstract
We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including non-convex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.
Original language | English (US) |
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Article number | 6618983 |
Pages (from-to) | 1043-1050 |
Number of pages | 8 |
Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: Jun 23 2013 → Jun 28 2013 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Keywords
- Deblurring
- Deconvolution
- Random Walk
- Spatially-Varying PSF
- Stochastic