Enhancement of medical images denoised by rician noise using total variation full fractional filtering

Abstract

Rician noise is one of the main distortion sources in magnetic resonance imaging (MRI) and in several other biomedical imaging techniques. It usually reduces the sharpness of the image, which makes diagnosis more difficult in practical applications. Our study uses the Total Variation Full Fractional (TVFF) method to reduce the influence of Rician noise. The concept of Caputo-type fractional derivatives is applied in both spatial and temporal variables and provides better noise reduction than the classical models. Thanks to the fractional-order approach, the TVFF method can reduce noise effectively, and the main anatomical and textural details remain almost unaffected. Tests on synthetic MRI data show clear improvement in common quality measures such as signal-to-noise ratio (SNR), structural similarity index (SSIM), root mean square error (RMSE), and edge preservation index (EPI). Overall, the results show that the TVFF method can be a reliable and practical tool for improving the visual and diagnostic quality of biomedical images affected by Rician noise.