Abstract: A medical diagnostic apparatus (A) generates medical diagnostic data which is reconstructed by an imager (B) into an electronic image representation. The electronic image representation includes an array of digital pixel values which represent a gray scale intensity of a man-readable image displayed on a video monitor (62). An image improving circuit (C) replaces each pixel value I(i,j) with an improved pixel value I'(i,j) defined as follows:I'(i,j)=G(i,j)[I(i,j)-I(i,j)]+I(i,j),where G(i,j) is a transfer function uniquely defined for each pixel and I is the mean of pixel values of neighboring pixels. The transfer function is based on a self-similarity value which is derived by comparing (i) a variation between the pixel value I(i,j) and pixel values in a first surrounding ring with (ii) a variation between the pixel value I(i,j) and pixel values in a second, larger surrounding ring. A zoom circuit (D) enlarges a selected portion of an improved image.

Abstract: Conventional radar uses the Fourier transform to generate a radar target image. Constraints on the use of Fourier methods requiring point scatterers to remain in their range cells and requiring Doppler frequency shifts for point scatterers to be stationary are impractical due to a moving object's inherent non-uniform motion and rotation. Time varying motion-induced Doppler frequency shift spectra represented with Fourier transform methods smears radar target image. Representing time-varying Doppler spectrum using joint time-frequency transform methods is desirable. Replacing conventional radar Fourier transform with a high resolution time-frequency transform, a 2-D range-Doppler Fourier image frame becomes a 3-D time-range-Doppler cube. By sampling in time, a time sequence of 2-D range-Doppler images with superior resolution can be viewed. Smears from time-variance of Doppler spectrum may be removed to enhance target image.

Abstract: Detection and extraction of unknown signal in noise may be important in radar When an unknown signal of a transient nature is received, representation in terms of basis functions, localized in both time and frequency, such as Gabor representation, may be very useful for signal detection. By using time-frequency decomposition, noise energy tends to spread across entire time-frequency domain, while signal energy often concentrates within a small region with a limited time interval and frequency band. Signal recognition in the time-frequency domain becomes easier than that in either time or frequency domain. By setting a CFAR threshold for and examining time-frequency Gabor coefficients which exceed the threshold, presence of a signal may be determined. CFAR time-frequency processing for detection and extraction of signals in noise improves detection and extraction performance for low Signal-to-noise-ratio (SNR) signals.