Abstract
A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
Highlights
As one of the important methods in medical image diagnosis, CT image can achieve a high performance of visualizing and measuring microstructure of apparatus and tissue on detecting small lesions
The underlying point spread function (PSF) of the CT scanner is often unavailable in practice
The above-mentioned three-layer feedforward neural network structure can be addressed to eliminate the effect of PSF and obtain the estimation of the original image. wlk(n) denotes the weight between the input layer and the hidden layer, l describes the index of the input neuron (l = 0, 1, . . . , L−1), wk(n) denotes the weight between the hidden layer and the output layer, and k describes the index of the hidden neuron (k = 0, 1, . . . , K)
Summary
As one of the important methods in medical image diagnosis, CT image can achieve a high performance of visualizing and measuring microstructure of apparatus and tissue on detecting small lesions. We will address the only problem by utilizing the degraded image only in order to obtain an estimation of the original scene and present a novel blind image restoration algorithm. Zhang et al [24] proposed several blind equalization algorithms based on neural network, which present better performances such as quick convergence speed and small steady error in eliminating intersignal interface. A neural network blind equalization, which makes use of the similarity between the image degradation process and ISI, is derived and utilized in conjunction with Zigzag coding to restore the original image.
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