Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT

Abstract: 

This paper presents a flexible framework for the challenging task of color-guided depth upsampling. Some state-of-the-art approaches apply an aligned RGB image for depth recovery. Unfortunately, these kinds of methods may result in texture copying artifacts and edge blurring artifacts. To address these difficulties, we propose an adaptive weighted least squares framework of choosing different guidance weight for variant conditions flexibly. First of all, in the framework, we propose a joint adaptive color weighting scheme in which the depth maps and color images jointly choose a proper weight term for diverse cases. Then, a patch-based smoothness measuring approach called patching-gradient method (PGM) is proposed to distinguish the discontinuities and smooth areas. Our PGM is robust to dense noise and preserve weak edges effectively. Quantitative and qualitative experiments on noisy ToF-like datasets demonstrate our frameworks effectiveness on suppressing both texture copying artifacts and edge blurring artifacts.

up
0 users have voted:

Paper Details

Authors:
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao
Submitted On:
20 April 2018 - 1:54am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Yuyuan Li
Paper Code:
IVMSP-P2.4
Document Year:
2018
Cite

Document Files

Depth map super-resolution, ToF, WLS, Patching-gradient method, De-noising

(60 downloads)

Subscribe

[1] Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao, "DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3085. Accessed: Aug. 19, 2018.
@article{3085-18,
url = {http://sigport.org/3085},
author = {Yuyuan LI; Jiarui Sun; Bingshu Wang; Yong Zhao },
publisher = {IEEE SigPort},
title = {DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT},
year = {2018} }
TY - EJOUR
T1 - DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT
AU - Yuyuan LI; Jiarui Sun; Bingshu Wang; Yong Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3085
ER -
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. (2018). DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT. IEEE SigPort. http://sigport.org/3085
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao, 2018. DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT. Available at: http://sigport.org/3085.
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. (2018). "DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT." Web.
1. Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3085