Denoiser-Based Projections for 2D Super-Resolution MRA

Jonathan Shani, Tom Tirer, Raja Giryes, Tamir Bendory

Research output: Contribution to journalArticlepeer-review


We study the 2D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that describe the statistics of the images of interest. In this work, we build on recent advances in image processing and harness the power of denoisers as priors for images. To estimate an image, we propose utilizing denoisers as projections and using them within two computational frameworks that we propose: projected expectation-maximization and projected method of moments. We provide an efficient GPU implementation and demonstrate the effectiveness of these algorithms through extensive numerical experiments on a wide range of parameters and images.

Original languageEnglish
Pages (from-to)621-629
Number of pages9
JournalIEEE Open Journal of Signal Processing
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


  • MRA
  • Method of momented
  • expectation minimization
  • projected gradient descent


Dive into the research topics of 'Denoiser-Based Projections for 2D Super-Resolution MRA'. Together they form a unique fingerprint.

Cite this