mlsanityData to 7 Oct 2026

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement

arXiv 2105.08040 ↗First posted 17 May 2021eess.IV cs.CV cs.LGcited by 17

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Abstract (arXiv)

Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging, including electron, fluorescence and deconvolution microscopy, optical diffraction tomography and functional neuroimaging.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 43 of its 84 references that are in the map. First posted on arXiv 17 May 2021.

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43 of 84 references.

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Citing papers per year: 2021: 1; 2022: 6; 2023: 1; 2024: 1; 2025: 7; 2026: 1.

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  2. 2022Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstructioncited by 23
  3. 2022SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imagingcited by 5
  4. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
  5. 2021Unsupervised Knowledge-Transfer for Learned Image Reconstructioncited by 4
  6. 2025Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstructioncited by 4
  7. 2025PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problemscited by 4
  8. 2025UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weightscited by 3
  9. 2024Fast MRI for All: Bridging Access Gaps by Training without Raw Datacited by 2
  10. 2022Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guaranteescited by 1
  11. 2022Multi-Weight Respecification of Scan-specific Learning for Parallel Imagingcited by 0
  12. 2022Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilingscited by 0
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  3. 2025Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encodingcited by 0
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  5. 2026Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priorscited by 0