Performance Comparison of Acoustic Lens based Photoacoustic Image Reconstruction with Algorithm based Reconstruction Techniques

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.

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Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051669 ◽

1974 ◽

Vol 32 ◽

pp. 330-331

Author(s):

R. A. Crowther

Keyword(s):

Image Reconstruction ◽

Finite Number ◽

Density Distribution ◽

Electron Micrographs ◽

Mathematical Problem ◽

Three Dimensional ◽

Ideal Case ◽

Dimensional Image ◽

General Object ◽

The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

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Three dimensional deblurring of transmitted-light brightfield micrographs

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100148654 ◽

1993 ◽

Vol 51 ◽

pp. 564-565

Author(s):

Santosh Bhattacharyya

Keyword(s):

Image Reconstruction ◽

Three Dimensional ◽

Transmitted Light ◽

Reconstruction Algorithms ◽

3D Image Reconstruction ◽

Structural Details ◽

Spread Function ◽

Early Testing ◽

Linearized Model ◽

Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

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