Angshul Majumdar

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Recently a new classification assumption was proposed in [1]. It assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the(More)
A recent work proposed a novel Group Sparse Classifier (GSC) that was based on the assumption that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The Group Sparse Classifier requires solving an NP hard group-sparsity promoting optimization problem. Thus a convex relaxation of the(More)
This paper proposes a new method for face recognition based on a multiresolution analysis tool called Digital Curvelet Transform. Multiresolution ideas notably the wavelet transform have been profusely employed for addressing the problem of face recognition. However, theoretical studies indicate, digital curvelet transform to be an even better method than(More)
The problem of recognizing a face from a single sample available in a stored dataset is addressed. A new method of tackling this problem by using the Fisherface method on a generic dataset is explored. The recognition scheme is also extended to multiscale transform domains like wavelet, curvelet and contourlet. The proposed method in the transform domain(More)
This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading(More)
In recent years, there has been a concerted effort to reduce the MR scan time. Signal processing research aims at reducing the scan time by acquiring less K-space data. The image is reconstructed from the subsampled K-space data by employing compressed sensing (CS)-based reconstruction techniques. In this article, we propose an alternative approach to(More)
This work addresses the problem of real-time online reconstruction of dynamic magnetic resonance imaging sequences. The proposed method reconstructs the difference between the previous and the current image frames. This difference image is sparse. We recover the sparse difference image from its partial k-space scans by using a nonconvex compressed sensing(More)
In parallel magnetic resonance imaging (MRI), the problem is to reconstruct an image given the partial K-space scans from all the receiver coils. Depending on its position within the scanner, each coil has a different sensitivity profile. All existing parallel MRI techniques require estimation of certain parameters pertaining to the sensitivity profile,(More)