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Photo-Realistic Single Image Super- resolution Using a Generative AdversarialNetworkChristian Ledig, Lucas Theis, Ferenc Husz ar, jose caballero , Andrew Cunningham,Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe the breakthroughs in accuracy and speed ofsingle image super- resolution using faster and deeper con-volutional neural networks, one central problem remainslargely unsolved: how do we recover the finer texture detailswhen we super-resolve at large upscaling factors? Thebehavior of optimization-based super- resolution methods isprincipally driven by the choice of the objective work has largely focused on minimizing the meansquared reconstruction error.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham,´
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