Non-negative sparse coding shrinkage for image denoising using normal inverse Gaussian density model

Shang, Li

doi:10.1016/j.imavis.2007.12.006

Cited by 18 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been applied to many applications, e:g., noise removing [19,20], image recovering [21], text detection [22], object tracking [23], object classification [24,25], face recognition [26][27][28][29][30], action recognition [6,[31][32][33], event analysis [10,11,34], and so on. For example, Elad and Aharon [19] addressed the image denoising problem by learning an over-complete dictionary from image patches.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Elad and Aharon [19] addressed the image denoising problem by learning an over-complete dictionary from image patches. In [20], Shang used an extended non-negative sparse coding scheme to remove noise from data. Dong et al [21] restored a degraded image by optimizing a set of optimal sparse coding coefficients from a dictionary.…”

Section: Related Workmentioning

confidence: 99%

“…(20), the parameter m of F XY is set to the relative distance between objects X and Y. This feature lacks of the distinguishability to identify the ''walking'' action type from the ''running'' action type.…”

Section: Behavior Typementioning

confidence: 99%

See 2 more Smart Citations

Modeling and recognizing action contexts in persons using sparse representation

Chiang

Hsieh

Chuang

et al. 2015

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and recognizing action contexts in persons using sparse representation

Chiang

Hsieh

Chuang

et al. 2015

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

“…Sparse representation models have recently been used in image understanding tasks such as texture segmentation and feature selection [18,19]. The sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one measures the signal reconstruction error and the other measures the signal sparsity.…”

Section: Sparse Representation and Discriminative Dictionarymentioning

confidence: 99%

Text detection in images using sparse representation with discriminative dictionaries

Zhao

Kwok

2010

Image and Vision Computing

View full text Add to dashboard Cite

“…The former defines a holistic sparsity on a whole data representation matrix, while the latter finds the sparsest representation of each data vector individually. SC has been widely used in numerous signal processing tasks, such as imaging denoising, texture synthesis, and image classification [26], [36], [48]. Nevertheless, the performance of SC deteriorates when data are corrupted.…”

mentioning

confidence: 99%

Tensor LRR and Sparse Coding-Based Subspace Clustering

Gao

Tien

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established stateof- the-art methods.

show abstract

Non-negative sparse coding shrinkage for image denoising using normal inverse Gaussian density model

Cited by 18 publications

References 21 publications

Modeling and recognizing action contexts in persons using sparse representation

Modeling and recognizing action contexts in persons using sparse representation

Text detection in images using sparse representation with discriminative dictionaries

Tensor LRR and Sparse Coding-Based Subspace Clustering

Contact Info

Product

Resources

About