PossibleExtension.txt

This work can be extended for many directions. 
Since I am busy doing other projects, I provide these potential ideas for you! 
Hope that you can publish much more paper than I expect!

1. It is worthy to investigate new weight matrix beyond the diagonal form, such as the 
correlation form [1], to further improve the color image denoising performance.

2. The proposed MCWNNM model can be extended for hyperspectral image analysis, 
which may contain hundreds of bands with complex noise statistics.

3. The idea of extending to color image denoising methods can be applied to other 
(amount of) gray image denosing methods.

4. The MCWNNM has the potential to deal with other low level vision problems in 
color images such as the background subtraction, and image inpainting tasks, just as
the [2] did.

5. The parameter ''Par.lambda'' can be designed as a 3-dimensional vector, each of 
which controlling the noise levels in R, G, and B channels, respectively. I believe that 
this will improve the performance and robustness of the MCWNNM method.

6. To be continued.


Hope all these ideas will help you to have a happy experimence with MCWNNM :)
I would be very appreciated if you could cite my paper (more is better :D) in your 
future publications :D
Good Luck! 

Reference:

[1] N. J. Higham. Computing the nearest correlation matrix: a problem from finance. 
     IMA Journal of Numerical Analysis, 22(3):329, 2002.

[2] Gu S, et al. Weighted nuclear norm minimization and its applications to low level vision. 
     International journal of computer vision, 2017, 121(2): 183-208.

Copyright @ Jun Xu, 26/07/2017.