your lambda is trainable because you don't know how much measurement noise you have you can also write down the derivative with respect to lambda so this is quite nice because I can basically propagate my gradients easily through this data consistency there so here's what you actually end up then with is you have an input image a complex valued input image you have your case based measurements and then you have a number of denoising layers which try to effectively remove aliasing from the image and then after these the