Mesh Denoising using Extended ROF Model withL1Fidelity

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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3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

Remote Sensing ◽

10.3390/rs13112145 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2145

Author(s):

Yawen Liu ◽

Bingxuan Guo ◽

Xiongwu Xiao ◽

Wei Qiu

Keyword(s):

Real Data ◽

Geometric Constraints ◽

Feature Point ◽

Feature Points ◽

Denoising Method ◽

3D Mesh ◽

Mesh Denoising ◽

Scene Structure ◽

Sharp Edges ◽

Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

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Image-Based 3D MESH Denoising Through A Block Matching 3D Convolutional Neural Network Filtering Approach

2020 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme46284.2020.9102938 ◽

2020 ◽

Author(s):

Gerasimos Arvanitis ◽

Aris S. Lalos ◽

Konstantinos Moustakas

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Block Matching ◽

3D Mesh ◽

Mesh Denoising ◽

Filtering Approach

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Image Compression Approach using Segmentation and Total Variation Regularization

International Journal of Computers ◽

10.46300/9108.2021.15.6 ◽

2021 ◽

Vol 15 ◽

pp. 43-47

Author(s):

Ahmad Shahin ◽

Walid Moudani ◽

Fadi Chakik

Keyword(s):

Image Compression ◽

Total Variation ◽

Image Data ◽

Smart Devices ◽

Multimedia Content ◽

Total Variation Regularization ◽

Second Stage ◽

Tv Regularization ◽

Rof Model

In this paper we present a hybrid model for image compression based on segmentation and total variation regularization. The main motivation behind our approach is to offer decode image with immediate access to objects/features of interest. We are targeting high quality decoded image in order to be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The Adaptive fuzzy c-means (AFcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decompression phase, the reverse process is applied in which the decoded image suffers from missing details due to the coarse segmentation. For this reason, we suggest the application of total variation (TV) regularization, such as the Rudin-Osher-Fatemi (ROF) model, to enhance the quality of the coded image. Our experimental results had shown that ROF may increase the PSNR and hence offer better quality for a set of benchmark grayscale images.

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