(untitled) (Journal of Nonlinear Modeling and Analysis, 2024)
The proposed fuzzy composition-based filtering method aims to remove a presence of fractal Brownian noise in the MR brain images. The fractional Brownian motion (FBM) noise is a continuous time Gaussian processed noise and its very difficult to identify the positions and range of noise density level, due to a smoothed noise. The projected fuzzy scheme encloses an equivalent fuzzy interference scheme, a fuzzy average procedure and a fuzzy composition procedure. The noise subtraction scheme has been confirmed to be the finest while the depiction is tainted by means of fractional Brownian motion. With an average o/p Peak Signal to Noise Ratio (PSNR) of 37.22 and an average noisy image PSNR of 20.28, the average PSNR rate has improved by 16.94. In addition, the average mean square error (MSE) rate has decreased from 609.48 to 12.33 percent. An experimental result confirms that the fuzzy filtering achieves an outstanding eminence of reinstated images in terms of PSNR and MSE without the assistance of noiseless depiction.