Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2003 Papers
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Video enhancement using per-pixel virtual exposures
ACM SIGGRAPH 2005 Papers
Multi-View Multi-Exposure Stereo
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Recovering high dynamic range radiance maps from photographs
ACM SIGGRAPH 2008 classes
Invertible motion blur in video
ACM SIGGRAPH 2009 papers
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Computers and Electrical Engineering
Sports video summarization based on motion analysis
Computers and Electrical Engineering
Computers and Electrical Engineering
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This paper proposes a probability formulation that unifies both single-image deblurring and multi-image denoising using variational inference. The proposed formulation is based on a theoretical analysis that compares denoising and deblurring in the same probabilistic framework, and supported by a practical approach that deal with general motion that creates HDR images in the presence of spatially varying motion. Based on this formulation, a new algorithm for deblurring a noisy and blurry image pair is presented. Besides, we provide also an approach that combines existing optical flow and image denoising techniques for High Dynamic Range imaging.