Modular ADMM-Based Strategies for Optimized Compression, Restoration, and Distributed Representations of Visual Data

Yehuda Dar, Alfred M. Bruckstein

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Iterative techniques are a well-established tool in modern imaging sciences, allowing to address complex optimization problems via sequences of simpler computational processes. This approach has been significantly expanded in recent years by iterative designs where explicit solutions of optimization subproblems were replaced by black-box applications of ready-to-use modules for denoising or compression. These modular designs are conceptually simple, yet often achieve impressive results. In this chapter, we overview the concept of modular optimization for imaging problems by focusing on structures induced by the alternating direction method of multipliers (ADMM) technique and their applications to intricate restoration and compression problems. We start by emphasizing general guidelines independent of the module type used and only then derive ADMM-based structures relying on denoising and compression methods. The wide perspective on the topic should motivate extensions of the types of problems addressed and the kinds of black boxes utilized by the modular optimization. As an example for a promising research avenue, we present our recent framework employing black-box modules for distributed representations of visual data.

Original languageEnglish
Title of host publicationHandbook of Mathematical Models and Algorithms in Computer Vision and Imaging
Subtitle of host publicationMathematical Imaging and Vision
PublisherSpringer International Publishing
Number of pages33
ISBN (Electronic)9783030986612
ISBN (Print)9783030986605
StatePublished - 1 Jan 2023
Externally publishedYes


  • Alternating direction method of multipliers (ADMM)
  • Distributed representations
  • Inverse problems
  • Modular optimization
  • Signal compression

ASJC Scopus subject areas

  • General Mathematics
  • General Computer Science


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