A Comparative Analysis of Edge-Preserving Approaches for Image Filtering

2021 ◽  
pp. 441-453
Author(s):  
Niveditta Thakur ◽  
Nafis Uddin Khan ◽  
Sunil Datt Sharma
Keyword(s):  
Comparative Analysis ◽  
Image Filtering ◽  
Edge Preserving

Guided image filtering in shape-from-focus: A comparative analysis

2021 ◽  
Vol 111 ◽  
pp. 107670
Author(s):  
Usman Ali ◽  
Ik Hyun Lee ◽  
Muhammad Tariq Mahmood
Keyword(s):  
Comparative Analysis ◽  
Image Filtering ◽  
Guided Image Filtering ◽  
Shape From Focus

Edge-preserving Image Filtering Based on Soft Clustering

2021 ◽  
pp. 1-1
Author(s):  
Yang Yang ◽  
Hongjun Hui ◽  
Lanling Zeng ◽  
Yan Zhao ◽  
Yongzhao Zhan ◽  
...  
Keyword(s):  
Image Filtering ◽  
Edge Preserving ◽  
Soft Clustering

scholarly journals Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

2019 ◽  
Vol 9 (15) ◽  
pp. 3122 ◽  
Author(s):  
Chengtao Zhu ◽  
Yau-Zen Chang
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Data Sets ◽  
Matching Accuracy ◽  
Edge Preserving ◽  
Novel Approach ◽  
Combination Scheme ◽  
Image Pairs ◽  
Guided Image Filtering ◽  
The Cost

Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.

Image filtering method using trimmed statistics and edge preserving

2018 ◽  
Vol 12 (7) ◽  
pp. 1086-1094 ◽  
Author(s):  
Weiling Cai ◽  
Ming Yang ◽  
Fengyi Song
Keyword(s):  
Image Filtering ◽  
Edge Preserving ◽  
Filtering Method

Scale-Aware Edge-Preserving Image Filtering via Iterative Global Optimization

2018 ◽  
Vol 20 (6) ◽  
pp. 1392-1405 ◽  
Author(s):  
Zhiqiang Zhou ◽  
Bo Wang ◽  
Jinlei Ma
Keyword(s):  
Global Optimization ◽  
Image Filtering ◽  
Edge Preserving

LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering

2013 ◽  
Vol 22 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Tianshuang Qiu ◽  
Aiqi Wang ◽  
Nannan Yu ◽  
Aimin Song
Keyword(s):  
Image Filtering ◽  
Edge Preserving ◽  
Local Linear

scholarly journals Two iterative methods for reverse image filtering

2021 ◽  
Author(s):  
Alexander G. Belyaev ◽  
Pierre-Alain Fayolle
Keyword(s):  
Internal Structure ◽  
Iterative Methods ◽  
Image Filtering ◽  
Black Box ◽  
Original Image ◽  
Edge Preserving ◽  
Image Filters

AbstractWe consider the problem of recovering an original image $${\varvec{x}}$$ x from its filtered version $${\varvec{y}}={\varvec{f}}({\varvec{x}})$$ y = f ( x ) , assuming that the internal structure of the filter $${\varvec{f}}(\cdot )$$ f ( · ) is unknown to us (i.e., we can only query the filter as a black-box and, for example, cannot invert it). We present two new iterative methods to attack the problem, analyze, and evaluate them on various smoothing and edge-preserving image filters.

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