Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. refers to portioning the images into small, disjointed parts simplifying processes of analysis examination. Rician speckle noise are different types in magnetic resonance imaging (MRI) that affect accuracy negatively. Therefore, enhancement has significant role MRI segmentation. This paper proposes novel f...

In this work we derive a novel density driven diffusion scheme for image enhancement. Our approach, called D3, is a semi-local method that uses an initial structure-preserving oversegmentation step of the input image. Because of this, each segment will approximately conform to a homogeneous region in the image, allowing us to easily estimate parameters of the underlying stochastic process thus ...

—We propose an image denoising method that exploits nonlocal image modeling, principal component analysis (PCA), and local shape-adaptive anisotropic estimation. The nonlocal modeling is exploited by grouping similar image patches in 3-D groups. The denoising is performed by shrinkage of the spectrum of a 3-D transform applied on such groups. The effectiveness of the shrinkage depends on the ab...

We provide an upper bound on the rate of convergence of the mean-squared error for global image denoising, and illustrate that this upper bound decays with increasing image size. Hence, global denoising is asymptotically optimal. At least in an oracle scenario this property does not hold for patch-based methods such as BM3D, thereby limiting their performance for large images. As observed in pr...

A natural scene statistics (NSS) based blind image denoising approach is proposed, where denoising is performed without knowledge of the noise variance present in the image. We show how such a parameter estimation can be used to perform blind denoising by combining blind parameter estimation with a state-of-the-art denoising algorithm. Our experiments show that for all noise variances simulated...