MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

Author:

Chung Hyungjin1ORCID,Lee Eun Sun2ORCID,Ye Jong Chul3ORCID

Affiliation:

1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

2. Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Dongjak-gu, Republic of Korea

3. Department of Mathematical Sciences, Department of Bio and Brain Engineering, Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP) grant by Korea government

National Research Foundation of Korea

KAIST Key Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Reference63 articles.

1. Noise2noise: Learning image restoration without clean data;lehtinen;Proc ICML,2018

2. Artifacts in magnetic resonance imaging: how it can really affect diagnostic image quality and confuse clinical diagnosis?

3. Image restoration using very deep convolutional encoder–decoder networks with symmetric skip connections;mao;Proc Adv Neural Inf Process Syst,2016

4. Image denoising with block-matching and 3D filtering

5. Noise2Score: Tweedie’s approach to self-supervised image denoising without clean images;kim;Proc Adv Neural Inf Process Syst,2021

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