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» A self-referencing level-set method for image reconstruction...
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111
Voted
ICIP
2001
IEEE
15 years 12 months ago
A self-referencing level-set method for image reconstruction from sparse Fourier samples
Jong Chul Ye, Yoram Bresler, Pierre Moulin
ISBI
2004
IEEE
15 years 11 months ago
Level Set Methods for Dynamic Tomography
In this paper, we propose a novel variational framework for the reconstruction of dynamic objects from sparse and noisy tomographic data. Using an object-based scene model, we dev...
Yonggang Shi, William Clement Karl
104
Voted
ICIP
2008
IEEE
16 years 28 min ago
Nonconvex compressive sensing and reconstruction of gradient-sparse images: Random vs. tomographic Fourier sampling
Previous compressive sensing papers have considered the example of recovering an image with sparse gradient from a surprisingly small number of samples of its Fourier transform. T...
Rick Chartrand
90
Voted
ICIP
2000
IEEE
15 years 12 months ago
POCS-Based Image Reconstruction from Irregularly-Spaced Samples
This paper presents a method for the reconstruction of a regularlysampled image from its irregularly-spaced samples. Such reconstruction is often needed in image processing and co...
Ryszard Stasinski, Janusz Konrad
103
Voted
ICIP
2006
IEEE
15 years 12 months ago
Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data
We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected s...
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar