Rician Denoising Research Articles

Non-monotone Boosted DC and Caputo Fractional Tailored Finite Point Algorithm for Rician Denoising and Deblurring

Non-monotone Boosted DC and Caputo Fractional Tailored Finite Point Algorithm for Rician Denoising and Deblurring

A first-order Rician denoising and deblurring model

In this paper, we propose a first-order variational model for the Rician denoising and deblurring. The model employs a recently developed regularizer that has proven to be effective in image restoration [32]. Due to this regularizer, our model is able to suppress the staircase effect, and more importantly, it helps preserve image contrast, which considerably reduces the blurring effect in restored images. We present the maximum-minimum principle of the model and give a more accurate convex approximation than the conventional one for the fidelity term. Augmented Lagrangian method is utilized to minimize the associated functional. Synthetic and real gray and color images are tested to demonstrate the features of our model.

Compressed Sensing MRI Reconstruction Using Generative Adversarial Network with Rician De-noising

Compressed Sensing MRI Reconstruction Using Generative Adversarial Network with Rician De-noising

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

BackgroundTo investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach.MethodsProspective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising.ResultsThe averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10−3 mm2/s (lateral) to 0.93 × 10−3 mm2/s (dorsal). RD varied from 0.34 × 10−3 mm2/s (dorsal) to 0.38 × 10−3 mm2/s (lateral) and AD varied from 1.96 × 10−3 mm2/s (lateral) to 2.11 × 10−3 mm2/s (dorsal).ConclusionsOur results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

Rician denoising and deblurring using sparse representation prior and nonconvex total variation

We propose a sparse representation based model to restore an image corrupted by blurring and Rician noise. Our model is composed of a nonconvex data-fidelity term and two regularization terms involving a sparse representation prior and a nonconvex total variation. The sparse representation prior, using image patches, provides restored images with well-preserved repeated patterns and small details, whereas the non-convex total variation enables the preservation of edges. Moreover, the regularization terms are mutually complementary in removing artifacts. To realize our nonconvex model, we adopt the penalty method and the alternating minimization method. The K-SVD algorithm is utilized for learning dictionaries. Numerical experiments demonstrate that the proposed model is superior to state-of-the-art models, in terms of visual quality and certain image quality measurements.

An Efficient Tailored Finite Point Method for Rician Denoising and Deblurring

An Efficient Tailored Finite Point Method for Rician Denoising and Deblurring

On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising

Modeling magnitude Magnetic Resonance Images (MRI) rician denoising in a Bayesian or generalized Tikhonov framework using Total Variation (TV) leads naturally to the consideration of nonlinear elliptic equations. These involve the so called $1$-Laplacian operator and special care is needed to properly formulate the problem. The rician statistics of the data are introduced through a singular equation with a reaction term defined in terms of modified first order Bessel functions. An existence theory is provided here together with other qualitative properties of the solutions. Remarkably, each positive global minimum of the associated functional is one of such solutions. Moreover, we directly solve this non--smooth non--convex minimization problem using a convergent Proximal Point Algorithm. Numerical results based on synthetic and real MRI demonstrate a better performance of the proposed method when compared to previous TV based models for rician denoising which regularize or convexify the problem. Finally, an application on real Diffusion Tensor Images, a strongly affected by rician noise MRI modality, is presented and discussed.