mlsanityData to 7 Oct 2026

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

arXiv 2104.12939 ↗First posted 27 Apr 2021eess.IV cs.CVcited by 6

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Abstract (arXiv)

We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly interpretable neural network architecture with learned parameters and meanwhile retains convergence guarantee as classical optimization algorithms. To improve reconstruction quality, the proposed ELDA also employs a new non-local feature mapping and an associated regularizer. We compare ELDA with several state-of-the-art deep image methods, such as RED-CNN and Learned Primal-Dual, on a set of LDCT reconstruction problems. Numerical experiments demonstrate improvement of reconstruction quality using ELDA with merely 19 layers, suggesting the promising performance of ELDA in solution accuracy and parameter efficiency.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 29 of its 130 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

29 of 130 references.

References · 29

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17 more
  1. 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2023Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstructioncited by 6
  2. 2024DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Imagescited by 5
  3. 2025SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasetscited by 1
  4. 2022Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Losscited by 0
  5. 2024A Learned Proximal Alternating Minimization Algorithm and Its Induced Network for a Class of Two-block Nonconvex and Nonsmooth Optimizationcited by 0
  6. 2026Transferable Optimization Network for Cross-Domain Image Reconstructioncited by 0