A novel way to denoise multispectral images is proposed via an anisotropic diffusion based partial differential equation (PDE). A coupling term is added to the divergence term and it facilitates the modelling of interchannel relations in multidimensional image data. A total variation function is used to model the intrachannel smoothing and gives a piecewise smooth result with edge preservation. The coupling term uses weights computed from different bands of the input image and balances the interchannel information in the diffusion process. It aligns edges from different channels and stops the diffusion transfer using the weights. Well-posedness of the PDE is proved in the space of bounded variation functions. Comparison with the previous approaches is provided to demonstrate the advantages of the proposed scheme. The simulation results show that the proposed scheme effectively removes noise and preserves the main features of multispectral image data by taking channel coupling into consideration.
Denoised image 
Comparison with other schemes (from ):
Tschumperle & Deriche 
If you use the images presented here kindly cite Reference  below.
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 V. B. S. Prasath. Weighted Laplacian differences based multispectral anisotropic diffusion. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011. doi:10.1109/IGARSS.2011.6050119
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