I have a datacube of dimensions 512x512x256
pixels, that I need to analyse. The data represent 256 2D images of the
same object in different frequencies.
The aim is to obtain an estimation of the object, deconvolved and
denoised. To go further than the basic denoising approches that don't
take care of the correlations between channels, I was told the wavelets
can help, but don't know what to do. How would you proceed ?
each channel (i.e. frequency), the 512x512 image is composed of: a
signal strongly correlated between channels and a noise that is stonger
than the signal in 90% of the image. The signal is composed of: a point
spread function, which is fairly known, convolved with the object, which
is different but strongly correlated from a channel to the other.