Spatially adaptive image deblurring based on nonlocal means

2010 ◽  
Author(s):  
Ming Zhao ◽  
Wei Zhang ◽  
Zhile Wang ◽  
Fugang Wang
Keyword(s):  
Image Deblurring ◽  
Nonlocal Means ◽  
Spatially Adaptive

scholarly journals A spatially adaptive nonparametric regression image deblurring

2005 ◽  
Vol 14 (10) ◽  
pp. 1469-1478 ◽  
Author(s):  
V. Katkovnik ◽  
K. Egiazarian ◽  
J. Astola
Keyword(s):  
Nonparametric Regression ◽  
Image Deblurring ◽  
Spatially Adaptive

scholarly journals A spatially adaptive Poissonian image deblurring

2005 ◽  
Author(s):  
A. Foi ◽  
S. Alenius ◽  
M. Trimeche ◽  
V. Katkovnik ◽  
K. Egiazarian
Keyword(s):  
Image Deblurring ◽  
Spatially Adaptive

scholarly journals Image Denoising Using Nonlocal Means with Shape-Adaptive Patches and New Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chenglin Zuo ◽  
Jun Ma ◽  
Hao Xiong ◽  
Lin Ran
Keyword(s):  
Image Denoising ◽  
Image Sensors ◽  
Initial Estimate ◽  
Nonlocal Means ◽  
Spatially Adaptive ◽  
Shape Adaptation ◽  
Residual Image ◽  
Method Noise ◽  
Ccd Image

Digital images captured from CMOS/CCD image sensors are prone to noise due to inherent electronic fluctuations and low photon count. To efficiently reduce the noise in the image, a novel image denoising strategy is proposed, which exploits both nonlocal self-similarity and local shape adaptation. With wavelet thresholding, the residual image in method noise, derived from the initial estimate using nonlocal means (NLM), is exploited further. By incorporating the role of both the initial estimate and the residual image, spatially adaptive patch shapes are defined, and new weights are calculated, which thus results in better denoising performance for NLM. Experimental results demonstrate that our proposed method significantly outperforms original NLM and achieves competitive denoising performance compared with state-of-the-art denoising methods.

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

Sign in / Sign up

Export Citation Format

Share Document