Wavelet-based Denoising of Magnetic Resonance Images Using Optimized Exponential Function Thresholding and Wiener Filter

Magnetic Resonance (MR) images have many applications in medical science and play an essential role in the diagnosis and treatment of diseases. However, unavoidable artifacts and noise reduce the resolution of these images. In this paper,  we propose a hybrid noise reduction framework using the wavelet transform, the exponential function thresholding, and the Wiener filter. In particular, we first employ the Genetic algorithm to optimize the exponential function coefficient. Furthermore, we adopt the Winner filter to increase the robustness of the proposed scheme against different types of noise, such as Gaussion and Rician noise. Some common performance measures, such as Mean Square Error (MSE) and Peak Signal-to-Noise-Ratio (PSNR), have been used to evaluate the performance of the proposed method compared to existing counterparts. The results show that the performance of the proposed hybrid method is better than the existing methods, such as universal thresholding and plain exponential function thresholding. For example, for human brain images with Gaussian noise, the obtained PSNR using the proposed method is 53.3947, while the PSNR value is 51.7532 using the universal threshold. Moreover, the results indicate that by using the Winner filter, we can effectively control the robustness against noise and image blurring.