Paper Title
Estimation of Noise Levels and Denoising of Mixed Noise in MRI Images Using Threshold Based DWT

Noise is an unwanted variation in the pixel values of an image. An important image preprocessing step is image denoising, which removes noises from the image. Although many image denoising methods have been developed, denoising remains a challenge because it results in side effects like artifacts and blurring the images. Magnetic resonance imaging (MRI) is widely accepted imaging technique for plenty of medical applications. MRI is corrupted by Rician noise, and Rician-Gaussian Noise mixture. In this paper a technique for estimating the noise level called Modified linear minimum mean square error (MLMMSE) is proposed. In order to remove the noise from MRI images. A combination of threshold based DWT with Modified K-Singular Value Decomposition (K-SVD) algorithm is proposed. DWT based denoising technique is best suited for MRI Images, because of energy compaction, sparsity, and multi resolution. To enhance the result, DWT is combined with improved K-SVD algorithm. In this work, for further denoising, along with existing thresholding techniques such as Visushrink, Neighshrink, Sureshirnk, one more modified technique called Neighsureshrink is also proposed. From the experimental results, it is observed that Neighsureshrink thresholding technique based Coif5 DWT in association with improved K-SVD performs better. Keywords- Medical Image Denoising, DWT, Improved K-SVD, Thresholding.