eng Oriental Scientific Publishing Company Biosciences Biotechnology Research Asia 0973-1245 2015-04-28 12 1 705 716 6102 article A. Sumaiya Begum 1 S. Poornachandra 2 Department of ECE, RMD Engineering College, Chennai, India. Department of ECE, SNS College of Engineering, Coimbatore, India. Curvelet transform for de-noising Magnetic Resonance images corrupted with Rician noise using a newly proposed technique called beta-trim shrinkage. In this paper beta-trim shrinkage is combined with Bayesian thresholding technique to recover the image corrupted with noise. The classical wavelet transform codes homogenous regions effectively. However for improved image perception edges need to be preserved. Curvelet transform is well suited for edge preservation. Curvelet transform offers a sharp detection of linear and curvilinear features thus providing visually high-resolution images. Experiments were performed on several images. Results show that a significant level of noise is reduced by the proposed beta-trim method using Bayes thresholding rule when compared to classical methods. An appreciably high value of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC) and fairly lesser value of MSE (Mean square error) are obtained by the proposed method. https://www.biotech-asia.org/vol12no1/curvelet-based-image-de-noising-using-beta-trim-shrinkage-for-magnetic-resonance-images/ Curvelet Transform; Wavelet Transform; USFFT; Wrapping; Shrinkage