Abstract
The fidelity of an image subjected to digital processing, such as a contour/texture highlighting process or a noise reduction algorithm, can be evaluated based on two types of criteria: objective and subjective, sometimes the two types of criteria being considered together. Subjective criteria are the best tool for evaluating an image when the image obtained at the end of the processing is interpreted by man. The objective criteria are based on the difference, pixel by pixel, between the original and the reconstructed image and ensure a good approximation of the image quality perceived by a human observer. There is also the possibility that in evaluating the fidelity of a remade (reconstructed) image, the pixel-by-pixel differences will be weighted according to the sensitivity of the human visual system. The problem of improving medical images is particularly important in assisted diagnosis, with the aim of providing physicians with information as useful as possible in diagnosing diseases. Given that this information must be available in real time, we proposed a solution for reconstructing the contours in the images that uses a modified Wiener filter in the wavelet domain and a nonlinear cellular network and that is useful both to improve the contrast of its contours and to eliminate noise. In addition to the need to improve imaging, medical applications also need these applications to run in real time, and this need has been the basis for the design of the method described below, based on the modified Wiener filter and nonlinear cellular networks.
Highlights
Norbert Wiener’s theory of optimal filtering of continuous signals is the basis of linear least squares linear error filters dependent on input data
The authors present the results of research work on improving image quality through two powerful procedures for improving images obtained by soft truncation of wavelet coefficients correlated with nonlinear cellular networks
New filters are proposed: one based on the use of the translation invariant wavelet transform, which mediates the results obtained by repeating this procedure for several sets of wavelet coefficients and method based on Wiener filtering in the wavelet domain
Summary
Norbert Wiener’s theory of optimal filtering of continuous signals is the basis of linear least squares linear error filters dependent on input data. Wiener studied in his 1949 paper “Extrapolation, Interpolation and Smoothing of Stationary Time Series”, considering stationary signals, the problem of estimation in terms of the least squares error in the continuous case of time series. The extension of Wiener’s theory to the discrete case is simple and has a great practical utility because it led to the implementation of this type of filter using digital signal processors or specialized circuits in this class. The coefficients of the Wiener filter are calculated so as to minimize the average square error between the filter output and the useful signal. The solutions include an acoustic model (AM) and proves its robustness and effectiveness through experimental evaluation
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