scholarly journals Fast Surface Mesh Denoising with Regularization and Edge Preservation

PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 2010001-2010002 ◽  
Author(s):  
Hui Huang ◽  
Uri Ascher
Keyword(s):  
Surface Mesh ◽  
Edge Preservation ◽  
Mesh Denoising

Surface mesh denoising via diffusing gradient field

2011 ◽  
Vol 49 (1) ◽  
pp. 104-109 ◽  
Author(s):  
Jianhuang Wu ◽  
Jinting Xu ◽  
Renbo Xia
Keyword(s):  
Gradient Field ◽  
Surface Mesh ◽  
Mesh Denoising

Surface mesh denoising with normal tensor framework

2012 ◽  
Vol 74 (4) ◽  
pp. 130-139 ◽  
Author(s):  
Shoichi Tsuchie ◽  
Masatake Higashi
Keyword(s):  
Surface Mesh ◽  
Mesh Denoising ◽  
Normal Tensor

A cascaded approach for feature-preserving surface mesh denoising

2012 ◽  
Vol 44 (7) ◽  
pp. 597-610 ◽  
Author(s):  
Jun Wang ◽  
Xi Zhang ◽  
Zeyun Yu
Keyword(s):  
Surface Mesh ◽  
Mesh Denoising ◽  
Feature Preserving

Enhancing the Performance of One Dimensional Median Filter

2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu
Keyword(s):  
Impulse Noise ◽  
Rank Order ◽  
Median Filter ◽  
Filter Method ◽  
Salt And Pepper Noise ◽  
Edge Preservation ◽  
Word Level ◽  
Look Ahead ◽  
Filter Architecture ◽  
Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD, Medical and Radar Data

2020 ◽  
Vol 15 (2) ◽  
pp. 144-196 ◽  
Author(s):  
Mohammad R. Khosravi ◽  
Sadegh Samadi ◽  
Reza Mohseni
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Video Compression ◽  
Nearest Neighbor ◽  
Radar Data ◽  
Edge Preservation ◽  
Edge Information ◽  
Video Codecs ◽  
Comparative Results ◽  
Interesting Area

Background: Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14. Materials and Methods: In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample. Results: Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem. Conclusion: Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.

scholarly journals Detection of altered hippocampal morphology in multiple sclerosis-associated depression using automated surface mesh modeling

2012 ◽  
Vol 35 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Stefan M. Gold ◽  
Mary-Frances O'Connor ◽  
Raja Gill ◽  
Kyle C. Kern ◽  
Yonggang Shi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Surface Mesh ◽  
Mesh Modeling ◽  
Hippocampal Morphology

scholarly journals Mesh Denoising via Adaptive Consistent Neighborhood

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 412
Author(s):  
Mingqiang Guo ◽  
Zhenzhen Song ◽  
Chengde Han ◽  
Saishang Zhong ◽  
Ruina Lv ◽  
...  
Keyword(s):  
Graph Cut ◽  
Underlying Surface ◽  
Geometric Features ◽  
Two Stage ◽  
Mesh Denoising ◽  
Normal Field ◽  
Stage Scheme ◽  
Complex Shapes ◽  
Patch Shift

In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.

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