A new image smoothing method based on a simple model of spatial processing in the early stages of human vision.

The difficulty of preserving edges is central to the problem of smoothing images. The main problem is that of distinguishing between meaningful contours and noise, so that the image can be smoothed without loss of details. Substantial efforts have been devoted to solving this difficult problem, and a plethora of filtering methods have been proposed in the literature. Non-linear filters have proved to be more efficient than their linear counterparts. Here, a new nonlinear filter for noise smoothing is introduced. This filter is based on the psychophysical phenomenon of human visual contrast sensitivity. Results on real images are presented to demonstrate the validity of our approach compared to other known filtering methods.

Analysis of dynamic nuclear cardiac images by covariance function.

A new method using the covariance function as a measure of functional similarity is presented for dynamic analysis of a sequence of scintigraphic cardiac images taken throughout the cardiac cycle. The similarity between the temporal response of pixels in a reference region of the scintigraphic image series and the temporal response of the remaining pixels in the image sequence is calculated. The resulting covariance image is a functional image representing regions with different temporal dynamics. A box-plot representation of this image permits better interpretation for clinical decision making. This analysis allows visualization of the ventricular emptying pattern, which may be useful in studying motion or conduction abnormalities. Compared to Fourier analysis, our method does not make assumption that the data are periodic and that the transition between the first and the last frame of the study is smooth. The proposed method has been performed in one normal patient and twenty patients with abnormal ventricular contraction patterns, and there is no computational difficulty in its implementation. A comparison with the Fourier analysis is performed.

A noise-filtering method using a local information measure.

A nonlinear-noise filtering method for image processing, based on the entropy concept is developed and compared to the well-known median filter and to the center weighted median filter (CWM). The performance of the proposed method is evaluated through subjective and objective criteria. It is shown that this method performs better than the classical median for different types of noise and can perform better than the CWM filter in some cases.

Entropic contrast enhancement.

A technique for the contrast enhancement of a picture is proposed. The method is derived from the entropy concept of information theory. The originality of the algorithm rests on the use of a local contrast to define the digital entropy. The basic idea of the treatment is to enhance the contrast by transforming the global entropy. The same technique can also be used for an adaptive smoothing processing.