Max Basler

TL;DR: This paper presents a decoder architecture able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for image super-resolution and semantic segmentation, and outperforms the state-of-the-art methods.

TL;DR: A deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms is optimized, which results in more realistic textures and sharper edges and outperforms other state-of-the-art algorithms.

TL;DR: Zhang et al. as discussed by the authors proposed an encoder architecture able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for image super-resolution and semantic segmentation.