Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art Denoising Algorithms
Nicola Pierazzo, Gabriele Facciolo
→ BibTeX
@article{ipol.2017.203,
    title   = {{Data Adaptive Dual Domain Denoising: a Method to
Boost State of the Art Denoising Algorithms}},
    author  = {Pierazzo, Nicola and Facciolo, Gabriele},
    journal = {{Image Processing On Line}},
    volume  = {7},
    pages   = {93--114},
    year    = {2017},
    doi     = {10.5201/ipol.2017.203},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2017.203}}
published
2017-05-24
reference
Nicola Pierazzo, and Gabriele Facciolo, Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art Denoising Algorithms, Image Processing On Line, 7 (2017), pp. 93–114. https://doi.org/10.5201/ipol.2017.203

Communicated by Antoni Buades
Demo edited by Gabriele Facciolo

Abstract

This article presents DA3D (Data Adaptive Dual Domain Denoising), a 'last step denoising' method that takes as input a noisy image and as a guide the result of any state-of-the-art denoising algorithm. The method performs frequency domain shrinkage on shape and data-adaptive patches. DA3D doesn't process all the image samples, which allows it to use large patches (64 x 64 pixels). The shape and data-adaptive patches are dynamically selected, effectively concentrating the computations on areas with more details, thus accelerating the process considerably. DA3D also reduces the staircasing artifacts sometimes present in smooth parts of the guide images. The effectiveness of DA3D is confirmed by extensive experimentation. DA3D improves the result of almost all state-of-the-art methods, and this improvement requires little additional computation time.

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