Figure 1:

(A) Deep CNN architecture for estimation of DL-VIF. The resulting reference-free DL-VIF metric provides estimation of motion-induced artifacts and distortion, as well as realism of the structural content by learning trends shown by the reference VIF trained against matched motion-free references. (B) DL-VIF was used as the cornerstone of a Deep Autofocus framework in which the trajectory of motion is estimated by iteratively finding the motion trajectory that optimizes the DL-VIF autofocus cost function. The complete autofocus cost function combines DL-VIF with a regularization term that encourages smooth, non-abrupt, motion.