Multichannel blind deconvolution in the simplest formulation: how to solve?

According to your acquisition model, latent image (f) remains same while the observed images are different due to different psf and noise models. One way to look at it, is a motion-blur problem where a sharp and noise-free image(f) is corrupted by the motion blur kernel. As this is an ill-posed problem, in most of the literature it's solved iteratively by estimating the blur kernel and the latent image. The way you solve this depends entirely on your objective function. For example in some papers IRLS is used to estimate the blur kernel. You can find a lot of literature on this.

• If you want to use Richardson Lucy Blind deconvolution, then use it on just one frame.
• One strategy can be in each iteration while recovering f, assign different weights for contribution from each g(observed images). You can incorporate different weights in the objective function or calculate them according to the estimated blur kernel.