Precise depth map upsampling and enhancement based on edge‐preserving fusion filters

2018 ◽  
Vol 12 (5) ◽  
pp. 651-658
Author(s):  
Ting‐An Chang ◽  
Jar‐Ferr Yang
Keyword(s):  
Depth Map ◽  
Edge Preserving ◽  
Depth Map Upsampling

Edge-Preserving Depth Map Upsampling by Joint Trilateral Filter

2018 ◽  
Vol 48 (1) ◽  
pp. 371-384 ◽  
Author(s):  
Kai-Han Lo ◽  
Yu-Chiang Frank Wang ◽  
Kai-Lung Hua
Keyword(s):  
Depth Map ◽  
Edge Preserving ◽  
Trilateral Filter ◽  
Depth Map Upsampling

A High Quality Depth Map Upsampling Method Robust to Misalignment of Depth and Color Boundaries

2013 ◽  
Vol 75 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Jaekwang Kim ◽  
Jaeho Lee ◽  
Seung-Ryong Han ◽  
Dowan Kim ◽  
Jongsul Min ◽  
...  
Keyword(s):  
Depth Map ◽  
High Quality ◽  
Depth Map Upsampling

Depth map upsampling using compressive sensing based model

2015 ◽  
Vol 154 ◽  
pp. 325-336 ◽  
Author(s):  
Longquan Dai ◽  
Haoxing Wang ◽  
Xiaopeng Zhang
Keyword(s):  
Compressive Sensing ◽  
Depth Map ◽  
Depth Map Upsampling

Joint-adaptive bilateral depth map upsampling

2014 ◽  
Vol 29 (4) ◽  
pp. 506-513 ◽  
Author(s):  
Joohyeok Kim ◽  
Gwanggil Jeon ◽  
Jechang Jeong
Keyword(s):  
Depth Map ◽  
Depth Map Upsampling

Depth map upsampling using depth local features

2014 ◽  
Vol 50 (3) ◽  
pp. 170-171 ◽  
Author(s):  
Y.S. Kang ◽  
S.B. Lee ◽  
Y.S. Ho
Keyword(s):  
Depth Map ◽  
Local Features ◽  
Depth Map Upsampling

scholarly journals A study of edge preserving filters in image matching

2021 ◽  
Vol 10 (1) ◽  
pp. 111-117
Author(s):  
Rostam Affendi Hamzah ◽  
A. F. Kadmin ◽  
S. F. A. Gani ◽  
K. A. Aziz ◽  
T. M. F. T. Wook ◽  
...  
Keyword(s):  
Image Matching ◽  
Evaluation System ◽  
Stereo Matching ◽  
Median Filter ◽  
Depth Map ◽  
Bilateral Filter ◽  
Low Noise ◽  
Disparity Map ◽  
Edge Preserving ◽  
Accurate Matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.

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