Abstract
In this report, we present an image evaluation method in which mutual information (MI) is employed as a metric for selecting the optimal wavelet basis function to be used in denoising planar nuclear images, with the objective of improving image quality. The higher the MI value, the better the image quality. We initially selected eight different wavelet basis functions for investigation in the present study. Subsequently, wavelet transforms were applied to planar images for denoising by employing the universal soft-thresholding method. Finally, the MI values of the wavelet-transformed images were computed for comparison. In this study, a computer-generated 2-D grid-pattern image and phantom images produced using a standard inkjet printer served as the original images. The results for the simulation and phantom images showed the same trend of ranking in terms of MI. The images processed by dmey wavelet showed the highest MI values. To validate the usefulness of the proposed method, the standard deviation rate and edge slope ratio of the processed images were calculated and compared. The results showed that the MI value can serve as an effective criterion for selecting the optimal wavelet basis function for image denoising. The results also showed that, of the eight wavelet basis functions investigated, dmey wavelet is the optimal basis function for denoising low-contrast planar images.
