List of Articles Sparse Representation

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  1 - A Study on Clustering for Clustering Based Image De-noising
  Hossein Bakhshi Golestani Mohsen Joneidi Mostafa Sadeghi
  10.7508/jist.2014.04.001
  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of i More

  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? The second, what data clustering method is suitable for de-noising? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms its competitors in terms of de-noising performance and execution time. Manuscript profile
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  2 - A New VAD Algorithm using Sparse Representation in Spectro-Temporal Domain
  Mohadese  Eshaghi Farbod Razzazi Alireza Behrad
  10.7508/jist.2019.01.007
  This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries More

  This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90.11% and 91.75% under -5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms. Manuscript profile
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  3 - A Self-Learning Single Image Super-Resolution by Considering Consistency in Adjacent Pixels
  M. Habibi A. Ahmadyfard H. hassanpour
  In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as traini More

  In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as training data. In training phase, we apply support vector regression (SVR) to model the relationship between the pair of low and high-resolution images. For each patch in the low-resolution image, sparse representation is extracted as a feature vector. In this paper, in order to reduce the edge blurring effects, we first separate edge pixels from non-edge pixels. In the smooth area, because of the similar colors around the each pixel, the center pixel value is determined by considering the reconstructed adjacent pixels. Experimental results show that the proposed method is quantitatively and qualitatively outperform the competitive super-resolution approaches. Manuscript profile