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The efficiency of the Non-Local means (NLM) image denoising algorithm relieson the identification of similar original pixels from noisy similar patches.Hence fine details and low-contrasted structures are badly recovered after theapplication of NLM. But as these structures tend to correspond to redundantones in the Fourier domain, NLM filtering in this domain allows one to betterdenoise them. A mixed space-frequency approach improves the denoisingperformances of NLM because it ensures that the information is redundantenough, in the spatial domain or in the frequency domain. Our approach issimple : it consists in running two times the NLM algorithm (firstly in thefrequency domain and secondly in the spatial domain). For fine textures andisolated points we get a better visual reconstruction than with the originalNLM. In terms of PSNR, the improvement can be over 1 dB. Our approach givesintermediate results between the original NLM and state-of-the-art methodswhile at the same time having moderate complexity and leading to few visualartifacts.

Un algorithme de d\'ebruitage Non-Local means espace-fr\'equence (A space-frequency Non-Local means image denoising algorithm)