PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
December 12, 2023IEEE Transactions on Radiation and Plasma Medical Sciences49 citationsOpen Access

CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

YLYiming LeiCNChuang NiuJZJunping Zhang

Key Points

Key points are not available for this paper at this time.

Abstract

This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lei et al. (2023) studied this question.

synapsesocial.com/papers/69d7b970d84d071b73f30ba9https://doi.org/10.1109/trpms.2023.3341903
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Split Bregman Method for L1-Regularized Problems2009 · 4,351 citations
  2. 2Diffusion Model for Generative Image Denoising2023 · 13 citations
  3. 3Image denoising with block-matching and 3D filtering2006 · 753 citations
  4. 4Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification2021 · 29 citations
  5. 5Noise Suppression With Similarity-Based Self-Supervised Deep Learning2022 · 102 citations