A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform

Radio Science ◽  
2021 ◽  
Author(s):  
Zhan Yan ◽  
Hang Yan ◽  
Tao Wang
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Filtering Method ◽  
Local Means ◽  
Interferometric Phase ◽  
Non Local

Mistuning identification in a bladed disk using wavelet packet transform

2017 ◽  
Vol 229 (3) ◽  
pp. 1275-1295 ◽  
Author(s):  
N. Jamia ◽  
P. Rajendran ◽  
S. El-Borgi ◽  
M. I. Friswell
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Bladed Disk

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Use of Wavelet Packet Transform in Characterization of Surface Quality

2007 ◽  
Vol 46 (15) ◽  
pp. 5152-5158 ◽  
Author(s):  
J. Jay Liu ◽  
Daeyoun Kim ◽  
Chonghun Han
Keyword(s):  
Surface Quality ◽  
Wavelet Packet ◽  
Wavelet Packet Transform
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