An Algorithm for Texture Removal and Defect Extraction

2019 ◽  
Author(s):  
Shuang Zheng ◽  
Dongmeng Yu ◽  
Yaonan Li ◽  
Heping Chen
Keyword(s):  
Texture Removal ◽  
Defect Extraction

scholarly journals Image smoothing based on global sparsity decomposition and a variable parameter

2021 ◽  
Author(s):  
Xiang Ma ◽  
Xuemei Li ◽  
Yuanfeng Zhou ◽  
Caiming Zhang
Keyword(s):  
Removal Rate ◽  
Variable Parameter ◽  
Smoothing Parameter ◽  
Smoothing Effect ◽  
Image Smoothing ◽  
Sparse Decomposition ◽  
Alternating Direction ◽  
Texture Removal ◽  
Ideal Algorithm ◽  
Better Than

AbstractSmoothing images, especially with rich texture, is an important problem in computer vision. Obtaining an ideal result is difficult due to complexity, irregularity, and anisotropicity of the texture. Besides, some properties are shared by the texture and the structure in an image. It is a hard compromise to retain structure and simultaneously remove texture. To create an ideal algorithm for image smoothing, we face three problems. For images with rich textures, the smoothing effect should be enhanced. We should overcome inconsistency of smoothing results in different parts of the image. It is necessary to create a method to evaluate the smoothing effect. We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems. A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter. Three evaluation measures: edge integrity rate, texture removal rate, and gradient value distribution are proposed to cope with the third problem. We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results. Experiments show that our algorithm is better than existing algorithms both visually and quantitatively. We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

Fuzzy selective voting classifier with defect extraction based on comparison within an image

2007 ◽  
Author(s):  
Toshifumi Honda ◽  
Ryo Nakagaki ◽  
Obara Kenji ◽  
Yuji Takagi
Keyword(s):  
Defect Extraction

Design of Paper Defect Extraction IP Core Based on FPGA

2014 ◽  
Vol 513-517 ◽  
pp. 435-438
Author(s):  
Hong She Dang ◽  
Li Wang
Keyword(s):  
Data Processing ◽  
Defect Detection ◽  
Image Acquisition ◽  
Parallel Architecture ◽  
Recognition System ◽  
Ip Core ◽  
Recognition Systems ◽  
Defect Extraction ◽  
Detection And Recognition

A set of paper defect extraction IP core based on FPGA is introduced in this paper. Relying on FPGA powerful data processing capabilities and parallel architecture, the paper image acquisition, pre-processing and defect extraction function were achieved in an IP core. The IP core has been testing and running in some paper defect detection and recognition systems. As a result the system was stable and reliable which could ensure to achieve a paper defect extraction function. And it contributes to the design of paper defect detection and recognition system.

A method of defect extraction for point clouds

2018 ◽  
Author(s):  
Yuta Muraki ◽  
Koji Nishio ◽  
Takayuki Kanaya ◽  
Ken-ichi Kobori
Keyword(s):  
Point Clouds ◽  
Defect Extraction

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

2019 ◽  
Vol 38 (7) ◽  
pp. 721-732 ◽  
Author(s):  
Y. Sun ◽  
H. Chen ◽  
J. Qin ◽  
H. Li ◽  
M. Wei ◽  
...  
Keyword(s):  
Surface Texture ◽  
Texture Removal
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