A new exponentially directional weighted function based CT image denoising using total variation

Abstract

Today, Computed tomography (CT) is one of the high efficient tools in medical science which helps to diagnose the human body. The presence of noise may degrade the visual quality of the CT images, especially for low contrast images. Therefore, we propose a method based on the modification of total variation (TV) for noise suppression in CT images which is helpful to preserve the clinically relevant details. The modification of TV is performed by introducing a new exponentially directional weighted function (EDWF), which is based on the difference between L1 and L2 norms over the exponential function. Furthermore, a numerical algorithm is designed to solve the minimization problem of EDWF using Split Bregman method. The experimental results of proposed scheme are visually analyzed over the real noise CT image, added noise in true CT images, and also on low contrast zoomed noisy CT image. Apart from visual analysis, the proposed scheme is also verified with some standard performance metrics (RMSE, PSNR, SSIM, ED, DIV and GMSD). The proposed scheme is also compared with some standard existing methods and it is observed that performance of proposed scheme is superior to existing methods in terms of visual quality and performance metrics.