Image denoising via patch based L1-norm principal component analysis

Xiao Ling; J. Paul Brooks

doi:10.1117/12.2584811

12 April 2021 Image denoising via patch based _L1-norm principal component analysis

Xiao Ling, J. Paul Brooks

Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 1173002 (2021) https://doi.org/10.1117/12.2584811
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Patch-based image denoising approaches have gained popularity recently. We propose an image denoising approach using subspaces that are fit using an L₁-norm criterion. This new approach is competitive with existing approaches in terms of objective error metrics and visual fidelity, and has the added benefit that it can be implemented in parallel for large-scale applications.

Conference Presentation

Citation Download Citation

Xiao Ling and J. Paul Brooks "Image denoising via patch based _L1-norm principal component analysis", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 1173002 (12 April 2021); https://doi.org/10.1117/12.2584811

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