Multi-Scale DCT Denoising
Nicola Pierazzo, Jean-Michel Morel, Gabriele Facciolo
→ BibTeX
@article{ipol.2017.201,
    title   = {{Multi-Scale DCT Denoising}},
    author  = {Pierazzo, Nicola and Morel, Jean-Michel and Facciolo, Gabriele},
    journal = {{Image Processing On Line}},
    volume  = {7},
    pages   = {288--308},
    year    = {2017},
    doi     = {10.5201/ipol.2017.201},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2017.201}}
published
2017-10-29
reference
Nicola Pierazzo, Jean-Michel Morel, and Gabriele Facciolo, Multi-Scale DCT Denoising, Image Processing On Line, 7 (2017), pp. 288–308. https://doi.org/10.5201/ipol.2017.201

Communicated by Julie Delon
Demo edited by Gabriele Facciolo

Abstract

DCT denoising is a classic low complexity method built in the JPEG compression norm. Once made translation invariant, this algorithm was still proven to be competitive at the beginning of this century. Since then, it has been outperformed by patch based methods, which are far more complex. This paper proposes a two-step multi-scale version of the algorithm that boosts its performance and reduces its artifacts. The multi-scale strategy decomposes the image in a dyadic DCT pyramid, which keeps noise white at all scales. The single scale denoising is then applied to all scales, thus giving multiple denoised versions of the low frequency coefficients of the denoised image. A 'multi-scale fusion' of these multiple estimates avoids the ringing artifacts resulting from the pyramid recomposition. The final algorithm attains a good PNSR and much improved visual image quality. It is shown to have a deficit of only 1dB with respect to state of the art algorithms, but its complexity is two orders of magnitude lower.

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