A non-parametric filter for digital image restoration, using cluster analysis

2004 ◽  
Vol 25 (8) ◽  
pp. 841-847 ◽  
Author(s):  
Héctor Allende ◽  
Jorge Galbiati
Keyword(s):  
Cluster Analysis ◽  
Image Restoration ◽  
Digital Image ◽  
Parametric Filter ◽  
Non Parametric

Evaluation of analystical procedures for density estimation from line-transect data: data grouping, data truncation and the unit of analysis

1993 ◽  
Vol 20 (4) ◽  
pp. 433 ◽  
Author(s):  
C Southwell ◽  
K Weaver
Keyword(s):  
Cluster Analysis ◽  
Survey Data ◽  
Density Estimation ◽  
Line Transect ◽  
Density Estimates ◽  
Unit Of Analysis ◽  
Line Transect Survey ◽  
Data Truncation ◽  
Data Grouping ◽  
Non Parametric

We examined three aspects of line-transect analytical procedures: data grouping, data truncation and the use of individuals or clusters as the analytical unit. Bias and precision of density estimation in relation to various levels of these factors were assessed for 4 types of line-transect estimator (simple parametric, generalised parametric, non-parametric and quasi-strip) using line-transect survey data from macropod populations of known density. The effect of data grouping on bias and precision varied between estimators. Bias was stable across all grouping levels tested for the simple parametric estimator, and stable across aU but the coarsest grouping level for the generalised parametric and non-parametric estimators, but varied substantially across the range of levels tested for the quasi-strip estimator. Precision improved as the number of grouping levels increased for all estimators tested, but the extent of improvement varied between estimators, and for the estimator most affected, improvement was marginal beyond intermediate grouping levels. Density estimates were generally more accurate and precise when analysed in ungrouped form than in grouped form. No effects of truncation on bias or precision were detected. Varying the analytical unit did not affect bias, but precision was significantly lower for cluster analysis than individual analysis for all estimators.

A Comparative Study of Image Deblurring Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4571-4579
Author(s):  
Rajbir Singh ◽  
Sumit Bansal
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Digital Image ◽  
Minimum Mean Square Error ◽  
Direct Methods ◽  
Structural Similarity ◽  
Single Step ◽  
Noise Model ◽  
Image Deconvolution ◽  
Indirect Methods

The method of recovering a true image from degraded one, to analyze that digital image and characteristics with no artifact errors is known as Image Restoration. These techniques are of two types: direct methods and indirect methods. Direct methods are those in which the results of image restoration are produced in one single step. Indirect methods are those in which the results of image restoration are produced after various steps. This method is termed as blind image deconvolution, when the known info is just the blurred digital image and no info about the (Point Spread Function) (PSF) or the degrading model. The target of the procedure is to recover both the latent (un-blurred) image and the blur kernel, simultaneously. In this paper, we presented a comprehensive research of image noise model,de-blurring methods, blur types, and a comparative study of various deblurring methods. We have implemented number experiments to study these methods according to their performance, (Peak Signal to Noise Ratio) PSNR, (structural similarity) SSIM, blur type, and (Minimum Mean Square Error) MMSE.

Constrained deconvolution of SPECT liver tomograms by direct digital image restoration

1985 ◽  
Vol 12 (1) ◽  
pp. 53-58 ◽  
Author(s):  
S. Webb ◽  
A. P. Long ◽  
R. J. Ott ◽  
M. O. Leach ◽  
M. A. Flower
Keyword(s):  
Image Restoration ◽  
Digital Image
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