Paper Info
Title
A Kalman Filter Approach for Denoising and Deblurring 3-D Microscopy Images
Abstract
This paper proposes a new method for removing noise and blurring from 3D microscopy images. The main contribution is the definition of a space-variant generating model of a 3-D signal, which is capable to stochastically describe a wide class of 3-D images. Unlike other approaches, the space-variant structure allows the model to consider the information on edge locations, if available. A suitable description of the image acquisition process, including blurring and noise, is then associated to the model. A state-space realization is finally derived, which is amenable to the application of standard Kalman filter as an image restoration algorithm. The so obtained method is able to remove, at each spatial step, both blur and noise, via a linear minimum variance recursive one-shot procedure, which does not require the simultaneous processing of the whole image. Numerical results on synthetic and real microscopy images confirm the merit of the approach.
Year
DOI
Venue
2013
10.1109/TIP.2013.2284873
IEEE Transactions on Image Processing
Keywords
Field
DocType
Kalman filters,image denoising,image restoration,medical image processing,stereo image processing,3D microscopy images,3D signal,Kalman filter approach,edge locations,image acquisition process,image deblurring,image denoising,image restoration algorithm,linear minimum variance recursive one-shot procedure,space-variant generating model,space-variant structure,state-space realization,Kalman filters,deconvolution,image restoration,optical microscopy,state-space methods
Computer vision,Deblurring,Non-local means,Image processing,Gaussian blur,Kalman filter,Image noise,Artificial intelligence,Image restoration,Digital image processing,Mathematics
Journal
Volume
Issue
ISSN
22
12
1057-7149
Citations 
PageRank 
References 
2
0.39
16
Authors
3
Name
Order
Citations
PageRank
Francesco Conte1236.39
A. Germani240152.47
Giulio Iannello341446.75