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In this paper, we address the problem of large-scale multi-view spectral clustering. In many real-world applications , data can be represented in various heterogeneous features or views. Different views often provide different aspects of information that are complementary to each other. Several previous methods of clustering have demonstrated that better(More)
In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location. Different from previous methods, we do not make any assumption about the upsampled multispectral image, but only assume that the fused image after downsampling should be close to the(More)
In this study, we propose a novel scheme for real time dynamic magnetic resonance imaging (dMRI) reconstruction. Different from previous methods, the reconstructions of the second frame to the last frame are independent in our scheme, which only require the first frame as the reference. Therefore, this scheme can be naturally implemented in parallel. After(More)
In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral (Ms) image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The(More)
In this paper, a calibrationless method is proposed for parallel magnetic resonance imaging (pMRI). It is motivated by the observation that the gradients of the aliased images are jointly sparse. Therefore, the pMRI problem can be formulated as a joint total variation regularization task. The field of view is finally obtained via a sum of square approach.(More)
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property forest sparsity. It exploits both intra- and inter- channel correlations(More)
Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and similarity computation of images. However, most existing methods still suffer(More)
The definition of the similarity measure is an essential component in image registration. In this paper, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by that the optimally registered images can be deeply sparsified in the gradient domain and frequency domain, with the separation of a sparse(More)
—In this paper, we present an efficient and robust image representation method that can handle misalignment, occlusion and big noises with lower computational cost. It is motivated by the sub-selection technique, which uses partial observations to efficiently approximate the original high dimensional problems. While it is very efficient, their method can(More)
Robust visual tracking of instruments is an important task in retinal microsurgery. In this context, the instruments are subject to a large variety of appearance changes due to illumination and other changes during a procedure, which makes the task very challenging. Most existing methods require collecting a sufficient amount of labelled data and yet(More)