An Analysis and Reduction of Fractional Brownian motion Noise in Biomedical Images Using Curvelet Transform and Various Filtering and Thresholding Techniques

The application of image restoration is not limited to the case of medical images especially in case of high resolution brain MRI images. The fractional Brownian motion noise present in these images affects certain important features which are needed for the proper diagnosis of brain diseases. Removal of fBm noise in these images is a kind of difficulty the researcher experiences. This paper investigates the various noise reduction techniques for reducing the fractional Brownian motion noise by using curvelet transform, various filtering techniques such as bilateral filter, trilateral filter, thresholding techniques like VisuShrink, NeighShrink and BayesShrink. The performance of all these techniques is analyzed using PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Metric), FD (Fractal Dimension), IEF (Image Enhancement Factor) and time elapsed. The performance of discrete curvelet transform, NeighShrinkand VisuShrink methods are found to be similar and relatively better than other techniques in terms of PSNR and SSIM. But curvelet transform requires increased computation time than other noise reduction techniques.