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User Manual for Model-based Imaging Inverse Problem

arXiv 2509.01572 ↗First posted 1 Sep 2025math.NA cs.CV cs.NAcited by 0

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Abstract (arXiv)

This user manual is intended to provide a detailed description on model-based optimization for imaging inverse problem. Theseproblems can be particularly complex and challenging, especially for individuals without prior exposure to convex optimization orinverse problem theory, like myself. In light of this, I am writing this manual to clarify and systematically organize the mathematicalrationale underlying imaging inverse problems. This manual might not be accurate in mathmatical notion but more focus on the logicalthinking on how to solve and proceed to solve the problems. If you want to think deep about something, try to raise questions! Thismanual is seaprated into four sections, aiming to answer the following four questions: (1) What is inverse imaging problem? (2) Why optimization is used to solve the inverse imaging problem? (3) How to solve the optimization problem? (4) How to implement the optimization algorithm in real imaging system?

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 1 Sep 2025.

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References · 11

  1. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  2. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
  3. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
  4. 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
  5. 2018Regularization by Denoising: Clarifications and New Interpretationscited by 103
  6. 2018Rank Minimization for Snapshot Compressive Imagingcited by 99
  7. 2019Image inpainting: A reviewcited by 89
  8. 2020Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imagingcited by 78
  9. 2014From Denoising to Compressed Sensingcited by 68
  10. 2020AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensingcited by 68
  11. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56

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