Spatially adaptive image deblurring based on nonlocal means

Author(s):  
Ming Zhao ◽  
Wei Zhang ◽  
Zhile Wang ◽  
Fugang Wang
2005 ◽  
Vol 14 (10) ◽  
pp. 1469-1478 ◽  
Author(s):  
V. Katkovnik ◽  
K. Egiazarian ◽  
J. Astola

Author(s):  
A. Foi ◽  
S. Alenius ◽  
M. Trimeche ◽  
V. Katkovnik ◽  
K. Egiazarian

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chenglin Zuo ◽  
Jun Ma ◽  
Hao Xiong ◽  
Lin Ran

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.


Author(s):  
Hakan Ancin

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.


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