Group-sparse representation with dictionary learning for medical image denoising and fusion
Recently, sparse representation has attracted a lot of interest in various areas. However, the
standard sparse representation does not consider the intrinsic structure, ie, the nonzero …
standard sparse representation does not consider the intrinsic structure, ie, the nonzero …
Total variation regularized RPCA for irregularly moving object detection under dynamic background
Moving object detection is one of the most fundamental tasks in computer vision. Many
classic and contemporary algorithms work well under the assumption that backgrounds are …
classic and contemporary algorithms work well under the assumption that backgrounds are …
Structured overcomplete sparsifying transform learning with convergence guarantees and applications
In recent years, sparse signal modeling, especially using the synthesis model has been
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …
Vector sparse representation of color image using quaternion matrix analysis
Traditional sparse image models treat color image pixel as a scalar, which represents color
channels separately or concatenate color channels as a monochrome image. In this paper …
channels separately or concatenate color channels as a monochrome image. In this paper …
Block-sparse RPCA for salient motion detection
Recent evaluation [2],[13] of representative background subtraction techniques
demonstrated that there are still considerable challenges facing these methods. Challenges …
demonstrated that there are still considerable challenges facing these methods. Challenges …
Sensing matrix optimization for block-sparse decoding
L Zelnik-Manor, K Rosenblum… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
Recent work has demonstrated that using a carefully designed sensing matrix rather than a
random one, can improve the performance of compressed sensing. In particular, a well …
random one, can improve the performance of compressed sensing. In particular, a well …
Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation
Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve
the quality of images for object detection, classification, and other subsequent applications. It …
the quality of images for object detection, classification, and other subsequent applications. It …
[HTML][HTML] Denoising images corrupted with impulse, Gaussian, or a mixture of impulse and Gaussian noise
A Awad - Engineering Science and Technology, an International …, 2019 - Elsevier
In this paper, a cascade of stages is used to denoise images corrupted with Gaussian noise,
impulse noise or a mixture of the two. The proposed method is based on removing the …
impulse noise or a mixture of the two. The proposed method is based on removing the …
[PDF][PDF] 字典学**模型, 算法及其应用研究进展
练秋生， 石保顺， 陈书贞 - 自动化学报, 2015 - aas.net.cn
摘要稀疏表示模型常利用训练样本学**过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单,
高效, 通用性**的字典学**算法是目前的主要研究方向之一, 也是信息领域的研究热点 …
高效, 通用性**的字典学**算法是目前的主要研究方向之一, 也是信息领域的研究热点 …
Foreground segmentation with tree-structured sparse RPCA
Background subtraction is a fundamental video analysis technique that consists of creation
of a background model that allows distinguishing foreground pixels. We present a new …
of a background model that allows distinguishing foreground pixels. We present a new …