A variational approach for Bayesian blind image deconvolution

By Aristidis C. Likas, N. Galatsanos. 2004

207 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Anat Levin
2. William T. Freeman
3. Frédo Durand
4. Brian Curless
5. Yair Weiss
6. Michael F. Cohen
7. C. Lawrence Zitnick
8. David M. Blei

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Jitendra Malik
5. Trevor Darrell
6. Xiaogang Wang
7. Andrew Zisserman
8. Kaiming He

In Signal Processing

1. Yair Weiss
2. C. Lawrence Zitnick
3. Frédo Durand
4. William T. Freeman
5. David M. Blei
6. Alp Kucukelbir
7. Jon D. McAuliffe
8. Ming-Hsuan Yang

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “…examples, variational inference is important to probabilistic models of videos (Chan and Vasconcelos 2009; Wang and Mori 2009), image denoising (Likas and Galatsanos 2004), tracking (Vermaak, Lawrence, and Pérez 2003; Yu and Wu 2005), place recognition and mapping for robotics (Cummins and…”
• Single Image Deblurring Using Motion Density Functions (2010, methodology): “In most recent work, image blur is modeled as the convolution of an unobserved latent image with a single, spatially invariant blur kernel [1,2,3,4,5,6,7,8,9,10,11].”
• Understanding and evaluating blind deconvolution algorithms (2009, background): “[8, 15]) but in most cases only symmetric kernels are considered since their phase is known to be zero.”