Hi, Thanks for you nice work. I think the work is based on score based model, and in my opinion, the score is the gradient of the log of the data distribution. In algorithm 1, you use f_φ to learn the score of I_t, actually I_t is the processed low resolution images which are the outputs of CPEM. While in the sampling process (i.e., Algorithm 2), the input of f_φ is v (i.e., low resolution images that are not processed), my question is, why I_t and v have same distribution?
Hi, Thanks for you nice work. I think the work is based on score based model, and in my opinion, the score is the gradient of the log of the data distribution. In algorithm 1, you use f_φ to learn the score of I_t, actually I_t is the processed low resolution images which are the outputs of CPEM. While in the sampling process (i.e., Algorithm 2), the input of f_φ is v (i.e., low resolution images that are not processed), my question is, why I_t and v have same distribution?