  The efficiency of the Non-Local means (NLM) image denoising algorithm relies
on the identification of similar original pixels from noisy similar patches.
Hence fine details and low-contrasted structures are badly recovered after the
application of NLM. But as these structures tend to correspond to redundant
ones in the Fourier domain, NLM filtering in this domain allows one to better
denoise them. A mixed space-frequency approach improves the denoising
performances of NLM because it ensures that the information is redundant
enough, in the spatial domain or in the frequency domain. Our approach is
simple : it consists in running two times the NLM algorithm (firstly in the
frequency domain and secondly in the spatial domain). For fine textures and
isolated points we get a better visual reconstruction than with the original
NLM. In terms of PSNR, the improvement can be over 1 dB. Our approach gives
intermediate results between the original NLM and state-of-the-art methods
while at the same time having moderate complexity and leading to few visual
artifacts.
