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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Identification of image and blur parameters in frequency domain using the EM algorithm.

E Anarim¹, H Ucar, Y Istefanopulos

¹Dept. of Electr. and Electron. Eng., Bogazici Univ., Istanbul.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

This study introduces a new method for identifying causal and semicausal autoregressive (AR) parameters and blur parameters in degraded images. The approach effectively restores images without prior knowledge of noise or point spread function (PSF).

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Area of Science:

Image processing
Signal processing
Computational imaging

Background:

Previous methods focused on semicausal autoregressive (AR) parameter identification for images with observation noise only.
Accurate identification of both causal and semicausal AR parameters and blur parameters is crucial for image restoration.

Purpose of the Study:

To propose a novel approach for identifying causal and semicausal AR parameters and blur parameters in degraded images.
To achieve this without requiring prior knowledge of observation noise power or the point spread function (PSF).

Main Methods:

Decomposition of the image into 1-D independent complex scalar subsystems using the unitary discrete Fourier transform (DFT).
Application of the expectation-maximization (EM) algorithm to each subsystem for identifying AR model and blur parameters of the transformed image.
Utilizing the least squares (LS) method to identify the AR parameters of the original image.

Main Results:

Successfully identified both causal and semicausal AR parameters and blur parameters.
The expectation-maximization (EM) algorithm yielded the restored image as a byproduct.
The proposed method does not require prior knowledge of noise variance or the point spread function (PSF).

Conclusions:

The developed method effectively identifies AR and blur parameters for degraded images.
This approach offers robust image restoration by overcoming limitations of previous methods.
The technique provides a significant advancement in blind image deconvolution and parameter estimation.