A topological loss function for image Denoising on a new BVI-lowlight dataset

Alexandra Malyugina*, Nantheera Anantrasirichai, David Bull

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Although image denoising algorithms have attracted significant research attention, surprisingly few have been proposed for, or evaluated on, noise from imagery acquired under real low-light conditions. Moreover, noise characteristics are often assumed to be spatially invariant, leading to edges and textures being distorted after denoising. Here, we introduce a novel topological loss function which is based on persistent homology. The method performs in the space of image patches, where topological invariants are calculated and represented in persistent diagrams. The loss function is a combination of ℓ1 or ℓ2 losses with the new persistence-based topological loss. We compare its performance across popular denoising architectures and loss functions, training the networks on our new comprehensive dataset of natural images captured in low-light conditions – BVI-LOWLIGHT. Analysis reveals that this approach outperforms existing methods, adapting well to complex structures and suppressing common artifacts.

Original languageEnglish
Article number109081
JournalSignal Processing
Volume211
Early online date30 Apr 2023
DOIs
Publication statusPublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Image dataset
  • Image denoising
  • Loss function
  • Persistent homology
  • TDA

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