A novel technique to quantify the signal-to-noise ratio (SNR) of magnetic resonance images is developed. The image SNR is quantified by estimating the amplitude of the signal spectrum using the autocorrelation function of just one single magnetic resonance image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. It is shown that the technique can be implemented in a highly efficient way for the magnetic resonance imaging system.
An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.
A new and robust parameter estimation technique, named Gaussian-Taylor interpolation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with piecewise cubic Hermite interpolation, quadratic spline interpolation, autoregressive moving average and moving average. Overall, the proposed estimations for noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree compared with the others.
An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.