Image recovery via transform learning and low-rank modeling: The power of complementary regularizers

Bihan Wen, Yanjun Li, Yoram Bresler

Research output: Contribution to journalArticlepeer-review


Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image/video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods.

Original languageEnglish (US)
Article number9042815
Pages (from-to)5310-5323
Number of pages14
JournalIEEE Transactions on Image Processing
StatePublished - 2020


  • Block matching
  • Collaborative filtering
  • Image denoising
  • Image inpainting
  • Image reconstruction
  • Machine learning
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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