Self-Supervised Image Denoising Methods: an Application in Fetal MRI

  • Ana Cláudia Souza Vidal de Negreiros UFERSA
  • Gilson Giraldi LNCC
  • Heron Werner PUC-Rio
  • Ítalo Messias Feliz Santos LNCC

Resumo


The process of image denoising in magnetic resonance imaging (MRI) is more and more common and important in the medical area. However, it is usual that state-of-the-art deep learning methods require pair images (clean and noisy ones) to train the models which poses limitations in practice. In this sense, this work applied two recent techniques that do not need a clean image to train the models and reached good results for denoising tasks. We applied the NOISE2NOISE (N2N) and the NOISE2VOID (N2V) learning approaches and compared the results for denoising tasks using a fetal MRI dataset. The results showed that the N2N method outperformed the N2V one, considering the Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE) evaluation metrics, and visual analysis.

Palavras-chave: image denoising, medical area, NOISE2NOISE, NOISE2VOID, fetal MRI

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Publicado
13/11/2023
NEGREIROS, Ana Cláudia Souza Vidal de; GIRALDI, Gilson; WERNER, Heron; SANTOS, Ítalo Messias Feliz. Self-Supervised Image Denoising Methods: an Application in Fetal MRI. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 137-141. DOI: https://doi.org/10.5753/wvc.2023.27546.