SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

Understand

By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart.

• Although such objective functions generate near-photorealistic results, their capability is limited, since they estimate the reconstruction error for an entire image in the same way, without considering any semantic information.
• In this paper, we propose a novel method to benefit from perceptual loss in a more objective way.
• We optimize a deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms.