L
Lu Gan
Researcher at Brunel University London
Publications - 95
Citations - 3077
Lu Gan is an academic researcher from Brunel University London. The author has contributed to research in topics: Compressed sensing & Filter bank. The author has an hindex of 17, co-authored 90 publications receiving 2757 citations. Previous affiliations of Lu Gan include University College of Engineering & University of Newcastle.
Papers
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Proceedings ArticleDOI
Block Compressed Sensing of Natural Images
TL;DR: This paper proposes and study block compressed sensing for natural images, where image acquisition is conducted in a block-by-block manner through the same operator, and shows that the proposed scheme can sufficiently capture the complicated geometric structures of natural images.
Proceedings ArticleDOI
Sparsity adaptive matching pursuit algorithm for practical compressed sensing
TL;DR: This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing, called the sparsity adaptive matching pursuit, which provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases.
Journal ArticleDOI
Fast and Efficient Compressive Sensing Using Structurally Random Matrices
TL;DR: Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed sensing framework, called Structurally Random Matrix (SRM), which has theoretical sensing performance comparable to that of completely random sensing matrices.
Proceedings Article
Fast compressive imaging using scrambled block Hadamard ensemble
Lu Gan,Thong T. Do,Trac D. Tran +2 more
TL;DR: A highly sparse and fast sampling operator based on the scrambled block Hadamard ensemble that offers universality and requires a near-optimal number of samples for perfect reconstruction in a single-pixel camera system.
Proceedings ArticleDOI
Distributed Compressed Video Sensing
TL;DR: Unlike conventional DVC schemes, the DISCOS framework can perform most encoding operations in the analog domain with very low-complexity, making it be a promising candidate for real-time, practical applications where the analog to digital conversion is expensive, e.g., in Terahertz imaging.