MRI is one of the most powerful techniques to study the internal structure of the body. MRI image quality is affected by various noises. Noises in MRI are usually thermal and mainly due to the motion of charged particles in the coil. Noise in MRI images also cause a limitation in the study of visual images as well as computer analysis of the images. In this paper, first, it is proved that proba...

Due to the disadvantage of large amounts of data computation and image quality degradation of classical reconstruction algorithm, a novel adaptive method of image reconstruction denoising based on compressive sensing is proposed. Firstly, the wavelet approximate coefficients and detail coefficients from the image noise are Gaussian distribution, and have different variances in different levels....

of the Dissertation Image and Signal Processing with Non-Gaussian Noise: EM-Type Algorithms and Adaptive Outlier Pursuit by Ming Yan Doctor of Philosophy in Mathematics University of California, Los Angeles, 2012 Professor Luminita A. Vese, Chair Most of the studies of noise-induced phenomena assume that the noise source is Gaussian because of the possibility of obtaining some analytical result...

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

This paper studies the sensitivity in Euclidean reconstruction from an image triplet taken by an uncalibrated camera mounted on a robot arm. The idea of such a reconstruction is closely related to that proposed in 44. In this paper, we focus on an intermediate step of the reconstruction procedure which requires estimating the screw axis that corresponds to the defective eigenvector of a 44 matr...

Contrast enhanced magnetic resonance angiography enables one to visualize the vascular system of the human body. Typically, using conventional measurement techniques, one three-dimensional volume of the vascular system can be measured during the passage of the contrast agent. More recently, medical doctors desire time-information of the blood-flow, or want to scan time-dependent phenomena. To e...

Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propos...

Image restoration is the process of reconstruction or recovering an image that has been corrupted by some degradation phenomenon. Degradation may occur due to motion blur, Gaussian blur, noise and camera mismatch. In this paper corrupted image have been recovered using Modified Lucy Richardson algorithm in the presence of Gaussian blur and motion blur. The performance of this algorithm has been...

introduction: monte carlo (mc) is the most common method for simulating virtual spect projections. it is useful for optimizing procedures, evaluating correction algorithms and more recently image reconstruction as a forward projector in iterative algorithms; however, the main drawback of mc is its long run time. we introduced a model based method considering the effect of body attenuation and i...

Frequency-domain diffusion imaging uses the magnitude and phase of modulated light propagating through a highly scattering medium to reconstruct an image of the spatially dependent scattering or absorption coefficients in the medium. An inversion algorithm is formulated in a Bayesian framework and an efficient optimization technique is presented for calculating the maximum a posteriori image. I...