Algorithmic versatility of SPF-regularization methods

Lixin Shen, Bruce W. Suter, Erin E. Tripp

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Sparsity promoting functions (SPFs) are commonly used in optimization problems to find solutions which are sparse in some basis. For example, the ℓ1-regularized wavelet model and the Rudin-Osher-Fatemi total variation (ROF-TV) model are some of the most well-known models for signal and image denoising, respectively. However, recent work demonstrates that convexity is not always desirable in SPFs. In this paper, we replace convex SPFs with their induced nonconvex SPFs and develop algorithms for the resulting model by exploring the intrinsic structures of the nonconvex SPFs. These functions are defined as the difference of the convex SPF and its Moreau envelope. We also present simulations illustrating the performance of a special SPF and the developed algorithms in image denoising.

Original languageEnglish (US)
Pages (from-to)43-69
Number of pages27
JournalAnalysis and Applications
Issue number1
StatePublished - Jan 2021


  • DC programming
  • Sparsity promoting functions
  • image denoising
  • proximity algorithms

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics


Dive into the research topics of 'Algorithmic versatility of SPF-regularization methods'. Together they form a unique fingerprint.

Cite this