Learning Optimal Nonlinearities for Iterative Thresholding Algorithms
نویسندگان
چکیده
منابع مشابه
Adaptive Iterative Thresholding Algorithms for Magnetoenceophalography (MEG)
We provide fast and accurate adaptive algorithms for the spatial resolution of current densities in MEG. We assume that vector components of the current densities possess a sparse expansion with respect to preassigned wavelets. Additionally, different components may also exhibit common sparsity patterns. We model MEG as an inverse problem with joint sparsity constraints, promoting coupling of n...
متن کاملFreely Available, Optimally Tuned Iterative Thresholding Algorithms for Compressed Sensing
We conducted an extensive computational experiment, lasting multiple CPU-years, to optimally select parameters for important classes of algorithms for finding sparse solutions of underdetermined systems of linear equations. We make the optimally tuned implementations freely available at sparselab.stanford.edu; they can be used ’out of the box’ with no user input: it is not necessary to select t...
متن کاملPerformance analysis for a class of iterative image thresholding algorithms
A performance analysis procedure that analyses the properties of a class of iterative image thresholding algorithms is described. The image under consideration is modeled as consisting of two maximum-entropy primary images, each of which has a quasi-Gaussian probability density function. Three iterative thresholding algorithms identified to share a common iteration architecture are employed for...
متن کاملIterative Hard Thresholding with Near Optimal Projection for Signal Recovery
Recovering signals that have sparse representations under a given dictionary from a set of linear measurements got much attention in the recent decade. However, most of the work has focused on recovering the signal’s representation, forcing the dictionary to be incoherent and with no linear dependencies between small sets of its columns. A series of recent papers show that such dependencies can...
متن کاملIterative Thresholding for Sparse Approximations
Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimum sparse expansion is known to be NP hard in general and non-optimal strategies such as Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit and Basis Pursuit De-noising are often called upon. These methods show good performance i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2016
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2016.2548245