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Multi-scale Feature-Preserving Smoothing of Images and Volumes on GPU

Nassim Jibai 1, 2
2 MAVERICK - Models and Algorithms for Visualization and Rendering
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
Abstract : Two-dimensional images and three-dimensional volumes have found their way into our life and became a staple ingredient of our artistic, cultural, and scientific appetite. Images capture and immortalize an instance such as natural scenes, through a photograph camera. Moreover, they can capture details inside biological subjects through the use of CT (computer tomography) scans, X-Rays, ultrasound, etc. Three-dimensional volumes of objects are also of high interest in medical imaging, engineering, and analyzing cultural heritage. They are produced using tomographic reconstruction, a technique that combine a large series of 2D scans captured from multiple views. Typically, penetrative radiation is used to obtain each 2D scan: XRays for CT scans, radio-frequency waves for MRI (magnetic resonance imaging), electron-positron annihilation for PET scans, etc. Unfortunately, their acquisition is influenced by noise caused by different factors. Noise in two-dimensional images could be caused by low-light illumination, electronic defects, low-dose of radiation, and a mispositioning tool or object. Noise in three-dimensional volumes also come from a variety of sources: the limited number of views, lack of captor sensitivity, high contrasts, the reconstruction algorithms, etc. The constraint that data acquisition be noiseless is unrealistic. It is desirable to reduce, or eliminate, noise at the earliest stage in the application. However, removing noise while preserving the sharp features of an image or volume object remains a challenging task. We propose a multi-scale method to smooth 2D images and 3D tomographic data while preserving features at a specified scale. Our algorithm is controlled using a single user parameter -- the minimum scale of features to be preserved. Any variation that is smaller than the specified scale is treated as noise and smoothed, while discontinuities such as corners, edges and detail at a larger scale are preserved. We demonstrate that our smoothed data produces clean images and clean contour surfaces of volumes using standard surface-extraction algorithms. Our method is inspired by anisotropic diffusion within the volume. We compute our diffusion tensors from the local continuous histograms of gradients around each pixel in images and around each voxel in volume. Since our smoothing method works entirely on the GPU, it is extremely fast.
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Contributor : Nassim Jibai <>
Submitted on : Sunday, November 4, 2012 - 12:18:39 AM
Last modification on : Thursday, July 9, 2020 - 9:44:48 AM
Document(s) archivé(s) le : Tuesday, February 5, 2013 - 2:30:11 AM


  • HAL Id : tel-00748064, version 1


Nassim Jibai. Multi-scale Feature-Preserving Smoothing of Images and Volumes on GPU. Image Processing [eess.IV]. Université de Grenoble, 2012. English. ⟨tel-00748064v1⟩



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