A performance comparison of two WS filters for image reconstruction technique under different image types

2012 ◽  
Author(s):  
Kanabadee Srisomboon ◽  
Supap Srisaiprai ◽  
Preecha Thongdit ◽  
Wilaiporn Lee ◽  
Vorapoj Pattanavijit
Keyword(s):  
Image Reconstruction ◽  
Performance Comparison ◽  
Reconstruction Technique ◽  
Image Reconstruction Technique ◽  
A Performance

scholarly journals A performance comparison of two PWS filters in different domain for image reconstruction technique under different image types

1970 ◽  
Vol 7 (2) ◽  
pp. 168-180
Author(s):  
Kanabadee Srisomboon ◽  
Supap Srisaiprai ◽  
Preecha Thongdit ◽  
Vorapoj Patanavijit ◽  
Wilaiporn Lee
Keyword(s):  
Image Reconstruction ◽  
Computation Time ◽  
Poor Performance ◽  
Spatial Domain ◽  
Performance Comparison ◽  
Aerial Images ◽  
Reconstruction Technique ◽  
Weighted Sum ◽  
Image Reconstruction Technique ◽  
Text Images

Due to many factors that can be degraded an image quality from the desired version. Image reconstruction application is the method that aims to recover those degradations based on mathematical and statistical models. Partition-based weighted sum (PWS) filtering is one of the most effective techniques for application of an image restoration and reconstruction. In this paper, we compare two PWS filters in both frequency and spatial domain under several image types. Two PWS filters include hard partitionbased weighted sum (HPWS) filter and subspace hard partition-based weighted sum (S-HPWS) filter. Five image types are considered including aerial images, human images, miscellaneous images, object images and text images. The simulation results show that the spatial domain HPWS filter offers the best performance when we apply to restore object image, but this filter not successful in term of memory usage and complexity of computation. Frequency domain S-HPWS filter, which required less memory and computation time using PCA technique to reduce size of data, offers good performance when we attempt to restore miscellaneous image. On the other hand, text image gets poor performance from all types of filters.

PEMBELAJARAN SIMULASI PENCITRAAN CT DENGAN MENGGUNAKAN PRINSIP REKONSTRUKSI CITRA DALAM SOFTWARE MATLAB

2015 ◽  
Vol 8 (3) ◽  
pp. 161
Author(s):  
Samuel Gideon
Keyword(s):  
Image Reconstruction ◽  
Ct Imaging ◽  
The Body ◽  
Reconstruction Technique ◽  
Imaging Features ◽  
Back Projection ◽  
Cross Sectional ◽  
Low Contrast ◽  
Image Reconstruction Technique ◽  
Imaging Algorithms

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Spectral Imaging ◽  
Reconstruction Technique ◽  
Imaging Method ◽  
Combined Approach ◽  
Bayesian Reconstruction ◽  
Image Reconstruction Technique ◽  
Bayesian Image Reconstruction

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.

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