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Journal Articles IEEE Signal Processing Letters Year : 2024

Automatic Tuning of Denoising Algorithms Parameters without Ground Truth

Arthur Floquet
  • Function : Author
  • PersonId : 1325757
Emmanuel Soubies
Duong-Hung Pham
Denis Kouamé
Denis Kouame

Abstract

Denoising is omnipresent in image processing. It is usually addressed with algorithms relying on a set of hyperparameters that control the quality of the recovered image. Manual tuning of those parameters can be a daunting task, which calls for the development of automatic tuning methods. Given a denoising algorithm, the best set of parameters is the one that minimizes the error between denoised and ground-truth images. Clearly, this ideal approach is unrealistic, as the ground-truth images are unknown in practice. In this work, we propose unsupervised cost functions — i.e., that only require the noisy image — that allow us to reach this ideal gold standard performance. Specifically, the proposed approach makes it possible to obtain an average PSNR output within less than 1% of the best achievable PSNR.
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Licence : CC BY - Attribution
Origin : Files produced by the author(s)
Licence : CC BY - Attribution

Dates and versions

hal-04344047 , version 1 (10-01-2024)

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Arthur Floquet, Sayantan Dutta, Emmanuel Soubies, Duong-Hung Pham, Denis Kouamé, et al.. Automatic Tuning of Denoising Algorithms Parameters without Ground Truth. IEEE Signal Processing Letters, 2024, 31, pp.381 - 385. ⟨10.1109/LSP.2024.3354554⟩. ⟨hal-04344047⟩
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