Dzung T . Nguyen | Vietnam National University, Hanoi (original) (raw)

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Papers by Dzung T . Nguyen

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010

Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and ab... more Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and abundance estimation. In this paper, we propose a new approach to endmember extraction, which takes advantage of the sparsity property of the linear representation of HSI's spectral vector. Sparsity is measured by the l0 norm of the abundance vector. It is also well known that l1

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010

Video error concealment is an important technique in video communication to recover corrupted par... more Video error concealment is an important technique in video communication to recover corrupted parts when erroneously transmitting compressed sequences over network. In this work, we propose a novel error concealment scheme by grouping similar patches in the temporal domain to construct a low-rank matrix and recover the missing areas by the matrix completion technique. Different from most state-ofthe-art algorithms which recover the lost blocks based on at least one clean frame (I-frame), the proposed algorithm can work for the most general case when all frames are violated. When having I-frames in the receiver, the proposed algorithm also achieves much higher PSNR compared with the Boundary Matching Algorithm (BMA) adopted in H.264.

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 2011

Spectral pixels in a hyperspectral image are known to lie in a low-dimensional subspace. The Line... more Spectral pixels in a hyperspectral image are known to lie in a low-dimensional subspace. The Linear Mixture Model states that every spectral vector is closely represented by a linear combination of some signatures. When no prior knowledge of the representing signatures available, they must be extracted from the image data, then the abundances of each vector can be determined. The whole process is often referred to as unsupervised endmember extraction and unmixing. The Linear Mixture Model can be extended to Sparse Mixture Model R=MS + N, where not only single pixels but the whole hyperspectral image has a sparse representation using a dictionary M made of the data itself, and the abundance vectors (columns of S) are sparse at the same locations. The endmember extraction and unmixing tasks then can be done concurrently by solving for a row-sparse abundance matrix S. In this paper, we pose a convex optimization problem, then using simultaneous sparse recovery techniques to find S. This approach promise a global optimum solution for the process, rather than suboptimal solutions of iterative methods which extract endmembers one at a time. We use l1l2 norm of S to promote row-sparsity in simultaneous sparse recovery, then impose additional hyperspectral constraints to abundance vectors (such as non-negativity and sum-to-one).

2009 43rd Annual Conference on Information Sciences and Systems, 2009

This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS)-a solu... more This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS)-a solution for Distributed Video Coding (DVC) based on the recently emerging Compressed Sensing theory. The DISCOS framework compressively samples each video frame independently at the encoder. However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with side information. In particular, along with global frame-based measurements, the DISCOS encoder also acquires local block-based measurements for block prediction at the decoder. Our interframe sparsity model mimics state-of-the-art video codecs: the sparsest representation of a block is a linear combination of a few temporal neighboring blocks that are in previously reconstructed frames or in nearby key frames. This model enables a block to be optimally predicted from its local measurements by l1-minimization. The DISCOS decoder also employs a sparse recovery with side information to jointly reconstruct a frame from its global measurements and its local block-based prediction. Simulation results show that the proposed framework outperforms the baseline compressed sensing-based scheme of intraframecoding and intraframe-decoding by 8 − 10dB. Finally, unlike conventional DVC schemes, our 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.

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

Video error concealment is a post-processing technique that conceals the errors in a decoded vide... more Video error concealment is a post-processing technique that conceals the errors in a decoded video sequence based on data available only at the decoder. Most of the current techniques adopt the approach that recovers the Motion Vector (MV) of a lost image block, uses that MV to look for data to fill in the blank then performs some refinements. We propose a method that does not rely on MV recovery, but essentially bases on sparse representation of image patches on local temporal dictionaries. Experiment results show a large improvement over Boundary Matching Algoritm (BMA), the standard method used in reference software for H.264 video codec.

MILCOM 2012 - 2012 IEEE Military Communications Conference, 2012

ABSTRACT In this paper, we propose a new spatial-temporal joint sparsity method for the identific... more ABSTRACT In this paper, we propose a new spatial-temporal joint sparsity method for the identification and detection of chemical plume in hyperspectral imagery. The proposed algorithm relies on two key observations: 1. each hyperspectral pixel can be approximately represented by a sparse linear combination of the training samples; and 2. neighborhood pixels from the same hyperspectral image as well as consecutive hyperspectral frames usually have similar spectral characteristics. By grouping these pixels into a joint group structure and forcing them to have the same sparsity support of the training samples, we effectively exclude the correlation of not only spatial but also time domain of the HSI data. Before the presence of this paper, almost no methods have made use of the temporal information for the detection of chemical plume in hyperspectral video data. Furthermore, the proposed method shows very competitive results with the Adaptive Matched Subspace Detector (AMSD) algorithm where the chemical types are predefined.

2011 18th IEEE International Conference on Image Processing, 2011

We propose a novel method for video error concealment, which is essentially a combination of non-... more We propose a novel method for video error concealment, which is essentially a combination of non-local grouping of image patches and low-rank tensor approximation. The proposed method, though does not require the knowledge of Motion Vectors (MVs) as in traditional video concealment techniques such as Boundary Matching Algorithm (BMA) and its derivations, gives striking results in restoration of sequences with high error rate without the key frames. The method can also be customized to work on single images, for example in image inpainting tasks.

2009 IEEE International Workshop on Multimedia Signal Processing, 2009

We propose a novel Layered Compressed Sensing (CS) approach for robust transmission of video sign... more We propose a novel Layered Compressed Sensing (CS) approach for robust transmission of video signals over packet loss channels. In our proposed method, the encoder consists of a base layer and an enhancement layer. The base layer is a conventionally encoded bitstream and transmitted without any error protection. The additional enhancement layer is a stream of compressed measurements taken across slices of video signals for error-resilience. The decoder regards the corrupted base layer as the side information (SI) and employs a sparse recovery with SI to recover approximation of lost packets. By exploiting the SI at the decoder, the enhancement layer is required to transmit a minimal amount of compressed measurements for error protection that is only proportional to the amount of lost packets. Simulation results show that both compression efficiency and error-resilience capacity of the proposed scheme are competitive with those of other state-of-the-art robust transmission methods, in which Wyner-Ziv (WZ) coders often generate an enhancement layer. Thanks to the soft-decoding feature of sparse recovery algorithms, our CS-based scheme can avoid the cliff effect that often occurs with other Wyner-Ziv based schemes when the error rate is over the error correction capacity of the channel code. In addition, our result suggests that compressed sensing is actually closer to source coding with decoder side information than to conventional source coding.

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010

Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and ab... more Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and abundance estimation. In this paper, we propose a new approach to endmember extraction, which takes advantage of the sparsity property of the linear representation of HSI's spectral vector. Sparsity is measured by the l0 norm of the abundance vector. It is also well known that l1

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010

Video error concealment is an important technique in video communication to recover corrupted par... more Video error concealment is an important technique in video communication to recover corrupted parts when erroneously transmitting compressed sequences over network. In this work, we propose a novel error concealment scheme by grouping similar patches in the temporal domain to construct a low-rank matrix and recover the missing areas by the matrix completion technique. Different from most state-ofthe-art algorithms which recover the lost blocks based on at least one clean frame (I-frame), the proposed algorithm can work for the most general case when all frames are violated. When having I-frames in the receiver, the proposed algorithm also achieves much higher PSNR compared with the Boundary Matching Algorithm (BMA) adopted in H.264.

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 2011

Spectral pixels in a hyperspectral image are known to lie in a low-dimensional subspace. The Line... more Spectral pixels in a hyperspectral image are known to lie in a low-dimensional subspace. The Linear Mixture Model states that every spectral vector is closely represented by a linear combination of some signatures. When no prior knowledge of the representing signatures available, they must be extracted from the image data, then the abundances of each vector can be determined. The whole process is often referred to as unsupervised endmember extraction and unmixing. The Linear Mixture Model can be extended to Sparse Mixture Model R=MS + N, where not only single pixels but the whole hyperspectral image has a sparse representation using a dictionary M made of the data itself, and the abundance vectors (columns of S) are sparse at the same locations. The endmember extraction and unmixing tasks then can be done concurrently by solving for a row-sparse abundance matrix S. In this paper, we pose a convex optimization problem, then using simultaneous sparse recovery techniques to find S. This approach promise a global optimum solution for the process, rather than suboptimal solutions of iterative methods which extract endmembers one at a time. We use l1l2 norm of S to promote row-sparsity in simultaneous sparse recovery, then impose additional hyperspectral constraints to abundance vectors (such as non-negativity and sum-to-one).

2009 43rd Annual Conference on Information Sciences and Systems, 2009

This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS)-a solu... more This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS)-a solution for Distributed Video Coding (DVC) based on the recently emerging Compressed Sensing theory. The DISCOS framework compressively samples each video frame independently at the encoder. However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with side information. In particular, along with global frame-based measurements, the DISCOS encoder also acquires local block-based measurements for block prediction at the decoder. Our interframe sparsity model mimics state-of-the-art video codecs: the sparsest representation of a block is a linear combination of a few temporal neighboring blocks that are in previously reconstructed frames or in nearby key frames. This model enables a block to be optimally predicted from its local measurements by l1-minimization. The DISCOS decoder also employs a sparse recovery with side information to jointly reconstruct a frame from its global measurements and its local block-based prediction. Simulation results show that the proposed framework outperforms the baseline compressed sensing-based scheme of intraframecoding and intraframe-decoding by 8 − 10dB. Finally, unlike conventional DVC schemes, our 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.

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

Video error concealment is a post-processing technique that conceals the errors in a decoded vide... more Video error concealment is a post-processing technique that conceals the errors in a decoded video sequence based on data available only at the decoder. Most of the current techniques adopt the approach that recovers the Motion Vector (MV) of a lost image block, uses that MV to look for data to fill in the blank then performs some refinements. We propose a method that does not rely on MV recovery, but essentially bases on sparse representation of image patches on local temporal dictionaries. Experiment results show a large improvement over Boundary Matching Algoritm (BMA), the standard method used in reference software for H.264 video codec.

MILCOM 2012 - 2012 IEEE Military Communications Conference, 2012

ABSTRACT In this paper, we propose a new spatial-temporal joint sparsity method for the identific... more ABSTRACT In this paper, we propose a new spatial-temporal joint sparsity method for the identification and detection of chemical plume in hyperspectral imagery. The proposed algorithm relies on two key observations: 1. each hyperspectral pixel can be approximately represented by a sparse linear combination of the training samples; and 2. neighborhood pixels from the same hyperspectral image as well as consecutive hyperspectral frames usually have similar spectral characteristics. By grouping these pixels into a joint group structure and forcing them to have the same sparsity support of the training samples, we effectively exclude the correlation of not only spatial but also time domain of the HSI data. Before the presence of this paper, almost no methods have made use of the temporal information for the detection of chemical plume in hyperspectral video data. Furthermore, the proposed method shows very competitive results with the Adaptive Matched Subspace Detector (AMSD) algorithm where the chemical types are predefined.

2011 18th IEEE International Conference on Image Processing, 2011

We propose a novel method for video error concealment, which is essentially a combination of non-... more We propose a novel method for video error concealment, which is essentially a combination of non-local grouping of image patches and low-rank tensor approximation. The proposed method, though does not require the knowledge of Motion Vectors (MVs) as in traditional video concealment techniques such as Boundary Matching Algorithm (BMA) and its derivations, gives striking results in restoration of sequences with high error rate without the key frames. The method can also be customized to work on single images, for example in image inpainting tasks.

2009 IEEE International Workshop on Multimedia Signal Processing, 2009

We propose a novel Layered Compressed Sensing (CS) approach for robust transmission of video sign... more We propose a novel Layered Compressed Sensing (CS) approach for robust transmission of video signals over packet loss channels. In our proposed method, the encoder consists of a base layer and an enhancement layer. The base layer is a conventionally encoded bitstream and transmitted without any error protection. The additional enhancement layer is a stream of compressed measurements taken across slices of video signals for error-resilience. The decoder regards the corrupted base layer as the side information (SI) and employs a sparse recovery with SI to recover approximation of lost packets. By exploiting the SI at the decoder, the enhancement layer is required to transmit a minimal amount of compressed measurements for error protection that is only proportional to the amount of lost packets. Simulation results show that both compression efficiency and error-resilience capacity of the proposed scheme are competitive with those of other state-of-the-art robust transmission methods, in which Wyner-Ziv (WZ) coders often generate an enhancement layer. Thanks to the soft-decoding feature of sparse recovery algorithms, our CS-based scheme can avoid the cliff effect that often occurs with other Wyner-Ziv based schemes when the error rate is over the error correction capacity of the channel code. In addition, our result suggests that compressed sensing is actually closer to source coding with decoder side information than to conventional source coding.