mlsanityData to 14 Oct 2026

Deep Unrolled Recovery in Sparse Biological Imaging

arXiv 2109.14025 ↗First posted 28 Sep 2021cs.CV cs.IT cs.LGcited by 10

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Abstract (arXiv)

Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well-suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here, we review the method of deep unrolling, and show how it improves source localization in several biological imaging settings.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

9 of 43 references.

References · 9

  1. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
  2. 2020Deep Unfolding Network for Image Super-Resolutioncited by 254
  3. 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 44
  4. 2019Deep learning in ultrasound imagingcited by 40
  5. 2020Learned SPARCOM: Unfolded Deep Super-Resolution Microscopycited by 11
  6. 2021Learned super resolution ultrasound for improved breast lesion characterizationcited by 7
  7. 2017SPARCOM: Sparsity Based Super-Resolution Correlation Microscopycited by 6
  8. 2018Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detectioncited by 3
  9. 2021Model-inspired Deep Learning for Light-Field Microscopy with Application to Neuron Localizationcited by 2

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Cited by year

Citing papers per year: 2022: 4; 2023: 3; 2024: 2; 2025: 1.

10 papers.

  1. 2022Model-Based Deep Learning: On the Intersection of Deep Learning and Optimizationcited by 50
  2. 2022Theoretical Perspectives on Deep Learning Methods in Inverse Problemscited by 22
  3. 2022Physics Embedded Machine Learning for Electromagnetic Data Imagingcited by 11
  4. 2024Glimpse: Generalized Locality for Scalable and Robust CTcited by 6
  5. 2023Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholdingcited by 4
  6. 2023Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Accelerationcited by 2
  7. 2022Unrolled algorithms for group synchronizationcited by 1
  8. 2023A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolutioncited by 1
  9. 2024Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopycited by 1
  10. 2025mSQUID: Model-Based Leanred Modulo Recovery at Low Sampling Ratescited by 1