N
Nir Sochen
Researcher at Tel Aviv University
Publications - 217
Citations - 7559
Nir Sochen is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Image processing & Image segmentation. The author has an hindex of 40, co-authored 213 publications receiving 7085 citations. Previous affiliations of Nir Sochen include Technion – Israel Institute of Technology & French Alternative Energies and Atomic Energy Commission.
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Free water elimination and mapping from diffusion MRI.
TL;DR: It is suggested that free water is not limited to the borders of the brain parenchyma; it therefore contributes to the architecture surrounding neuronal bundles and may indicate specific anatomical processes.
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A general framework for low level vision
Nir Sochen,Ron Kimmel,R. Malladi +2 more
TL;DR: A new geometrical framework based on which natural flows for image scale space and enhancement are presented, which unifies many classical schemes and algorithms via a simple scaling of the intensity contrast, and results in new and efficient schemes.
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Image enhancement and denoising by complex diffusion processes
TL;DR: It is proved that the imaginary part is a smoothed second derivative, scaled by time, when the complex diffusion coefficient approaches the real axis, and developed two examples of nonlinear complex processes, useful in image processing.
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Forward-and-backward diffusion processes for adaptive image enhancement and denoising
TL;DR: The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features, resulting in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.
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Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images
Ron Kimmel,R. Malladi,Nir Sochen +2 more
TL;DR: The geometric framework and the general Beltrami flow are applied to feature-preserving denoising of images in various spaces to propose enhancement techniques that selectively smooth images while preserving either the multi-channel edges or the orientation-dependent texture features in them.