Table 1.

A summary of the tested methods with a description of their performance.

Spectral denoising technique	PROS	CONS
Savitzky-Golay	Very low signal distortion, short computational time	SNR gain lower than one order of magnitude, two parameters optimization
Fourier-Transform	Very low signal distortion, easy to implement, short computational time, easy to optimize	SNR gain lower than one order of magnitude
PCA	Significant SNR gain and reasonable signal distortion	Medium difficulty algorithm, time and memory consuming computations
MNF	Significant SNR gain and reasonable signal distortion	Difficult algorithm, hard to implement, time and memory consuming computations
Wavelets	Very low signal distortion	SNR gain around one order of magnitude, time consuming calculation and optimization
Spatial denoising technique	PROS	CONS
Fourier-Transform	Good pSNR gain, high SSIM, easy to implement, reasonable computation time, easy to optimize	Image artifacts
Mean Filter	Good SSIM, easy to implement, low computational time	Mild pSNR gain
Median Filter	Good SSIM, easy to implement, low computational time	Mild pSNR gain
Gauss Filter	Good SSIM, easy to implement, low computational time	Mild pSNR gain
Weighted Mean Filer	Good SSIM, easy to implement, low computational time	Mild pSNR gain
Wavelets	Noticeable pSNR gain	Low pSNR gain and SSIM, time consuming calculation and optimization, image artifacts
Deep Neural Networks	Reasonable pSNR and SSIM	Difficult algorithm to train