Thresholded smoothed-l0(SL0) dictionary learning for sparse representations

نویسندگان

  • Hadi Zayyani
  • Massoud Babaie-Zadeh
چکیده

In this paper, we suggest to use a modified version of Smoothed-!0 (SL0) algorithm in the sparse representation step of iterative dictionary learning algorithms. In addition, we use a steepest descent for updating the non unit columnnorm dictionary instead of unit column-norm dictionary. Moreover, to do the dictionary learning task more blindly, we estimate the average number of active atoms in the sparse representation of the training signals, while previous algorithms assumed that it is known in advance. Our simulation results show the advantages of our method over K-SVD in terms of complexity and performance.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Thresholded Smoothed- (sl0) Dictionary Learning for Sparse Representations

In this paper, we suggest to use a modified version of Smoothed0 (SL0) algorithm in the sparse representation step of iterative dictionary learning algorithms. In addition, we use a steepest descent for updating the non unit columnnorm dictionary instead of unit column-norm dictionary. Moreover, to do the dictionary learning task more blindly, we estimate the average number of active atoms in t...

متن کامل

Image Super-Resolution Based on Sparsity Prior via Smoothed l0 Norm

In this paper we aim to tackle the problem of reconstructing a high-resolution image from a single low-resolution input image, known as single image super-resolution. In the literature, sparse representation has been used to address this problem, where it is assumed that both low-resolution and high-resolution images share the same sparse representation over a pair of coupled jointly trained di...

متن کامل

Sparse Recovery using Smoothed $\ell^0$ (SL0): Convergence Analysis

Finding the sparse solution of an underdetermined system of linear equations has many applications, especially, it is used in Compressed Sensing (CS), Sparse Component Analysis (SCA), and sparse decomposition of signals on overcomplete dictionaries. We have recently proposed a fast algorithm, called Smoothed l (SL0), for this task. Contrary to many other sparse recovery algorithms, SL0 is not b...

متن کامل

Spectral unmixing using nonnegative matrix factorization with smoothed L0 norm constraint

Sparse nonnegative matrix factorization (NMF) is exploited to solve spectral unmixing. Firstly, a novel model of sparse NMF is proposed, where the smoothed L0 norm is used to control the sparseness of the factors corresponding to the abundances. Thus, one need not set the degree of the sparseness in prior any more. Then, a gradient based algorithm NMF-SL0 is utilized to solve the proposed model...

متن کامل

Smoothed l0 Norm Regularization for Sparse-View X-Ray CT Reconstruction

Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artifacts, and improve noise properties. In this work, we present an iterative reconstruction approach...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009