.png){kind=icon}
.png){kind=icon}
Return
A novel robust depth estimation method based on optimal region selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.measurement.2019.106928.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a novel robust depth estimation method based on optimal region selection is proposed with improved anti-noise capability and structural retention. In particular, this new scheme provides the practitioners with a better de-noising ability by means of improving the non-subsampled contourlet transform (NSCT) features. Moreover, an optimal region selection technique is developed to further suppress the noise in focus measure. In order to make the features more prominent, the derivatives of features along optical axis are normalized for weighting in optimal region selection process. Experimental results demonstrate that the proposed method has superiority on better anti-noise ability, higher structural retention performance, compared with the existing representative methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Shape from focus
Depth estimation
Robustness
Non-subsampled contourlet transform
Focus measure
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.