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Registration of images from different modalities in the presence of intra-image fluctuation and noise contamination is a challenging task. The accuracy and robustness of the deformable registration largely depend on the definition of appropriate objective function, measuring the similarity between the images. Among them the multi-dimensional modality independent neighbourhood descriptor (MIND) is...
Since the significant intensity variations existed between different modal images, the deformable registration is still very challenging. In this paper, in order to alleviate the variations deficiency and attain robust alignment, we propose a multi-dimensional tensor based modality independent neighbourhood descriptor (tMIND) to measure the similarity between the images. The tMIND compares the neighboring...
Image inpainting is a classical inverse ill-posed problem. In this paper, we introduce a multi-filters guided low-rank tensor coding as a priori information to tackle it. The key innovation is to formulate multiple feature-domain tensors by convoluting the target image with multi-filters. Furthermore, by exploring a low-rank tensor coding, it can reduce the redundancy between sparse feature vectors...
Image recovery from undersampled data has always been a challenging and fascinating task due to its implicit ill-posed nature and significance accompanied with the emerging compressed sensing (CS) theory. This paper proposes a novel Gradient based Dictionary Learning method for CT image Reconstruction (GradDL-CT), which alleviates the drawback of the popular total variation (TV) regularization by...
This paper presents a novel structure gradient and texture decor relating regularization (SGTD) for image decomposition. The motivation of the idea is under the assumption that the structure gradient and texture components should be properly decor related for a successful decomposition. The proposed model consists of the data fidelity term, total variation regularization and the SGTD regularization...
Initialization sensitivity usually occurs in dictionary learning algorithm for image decomposition. In this paper, we propose an adaptive dictionary learning algorithm by promoting structural incoherence at the stage of dictionary updating. The structural incoherence based dictionary learning (SIDL) method guides the cartoon and texture parts to be more properly represented by two incoherent dictionaries...
The combination of sparse coding and manifold learning has received much attention recently. However, the computational complexity of the resulting optimization problem hinders its practical application. In this paper, an augmented Lagrangian method is proposed to address this issue, which first transforms the unconstrained problem to an equivalent constrained problem and then an alternating direction...
A long-standing practical problem lies in achieving magnetic resonance thermometry (MRT) with high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases. To solve this problem, a novel method based on a partial separable function (PSF) model was proposed by exploiting the data redundancy. In this PSF model, two datasets (image data...
In diffraction tomography, gridding is often required to interpolate the non-Cartesian sampled data into a Cartesian coordinate. In this paper, the iterative next-neighbor regridding (INNG) algorithm is used to meet the need. Nevertheless, as well as in other gridding algorithms, interpolating non-Cartesian data to a Cartesian grid introduces errors, resulting in artifacts. Considering that total...
In clinical Magnetic Resonance Imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. In this paper we proposed a reconstruction algorithm by applying contourlet thresholding in inverse scale space flows. We improved the inverse scale space with the noise item in which...
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