Abstract: A method for retrieving original intact characteristics of images heavily polluted by Gaussian impulse noises is based on the fuzzy automata theory retrieves the characteristics of images heavily corrupted by Gaussian impulse noises. The retrieved characteristics are represented with fuzzy sets, each of which is also called a fuzzy partition of the image histogram. The membership functions are used for noise filtering, multi-level thresholding, segmentation, and edge detection. This invention further utilizes the result images of filtering and thresholding to perform the second phase processing. After two-phase filtering, the signal-to-noise ratio SNR and the peak signal to noise ratio PSNR are increased up to 10 db for images polluted by noises higher than 50%. Having very low complexity of processing time and memory space, the kernel operation of the algorithm can be implemented with super-scalar super-pipelined 16-bit floating-point processing unit and achieve 24-bit true color results.

Abstract: A method for retrieving original intact characteristics of images heavily polluted by Gaussian impulse noises is based on the fuzzy automata theory retrieves the characteristics of images heavily corrupted by Gaussian impulse noises. The retrieved characteristics are represented with fuzzy sets, each of which is also called a fuzzy partition of the image histogram. The membership functions are used for noise filtering, multi-level thresholding, segmentation, and edge detection. This invention further utilizes the result images of filtering and thresholding to perform the second phase processing. After two-phase filtering, the signal-to-noise ratio SNR and the peak signal to noise ratio PSNR are increased up to 10 db for images polluted by noises higher than 50%. Having very low complexity of processing time and memory space, the kernel operation of the algorithm can be implemented with super-scalar super-pipelined 16-bit floating-point processing unit and achieve 24-bit true color results.