Hi,

According to our experience, it is suggested to pre-train μ first by using L2 Loss on both gray and RGB images.

As for gray images, after pre-training μ, the whole framework can be trained end-to-end by using the final loss. (There is no need to pre-train σ_μ and σ_n specifically. That means it only needs two stage to train DBSN.)

As for RGB images, it is also suggested to pre-train σ_μ and σ_n specifically. (Because for each position in RGB image, we operate a 3x3 matrix, and pre-training phases can benefit to convergence. That means it needs three stage to train DBSN.)