Can I read Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations on EtoBox?

Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations by Zhang, Zhilu; Wang, Ruohao; Zhang, Hongzhi; Chen, Yunjin; Zuo, Wangmeng is a scholarly article available to read on EtoBox.

What is Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations about?

In this paper, we consider two challenging issues in reference-based super-resolution (RefSR), (i) how to choose a proper reference image, and (ii) how to learn real-world RefSR in a self-supervised manner. Particularly, we present a novel self-supervised learning approach for real-world image SR from observations at dual camera zooms (SelfDZSR). Considering the popularity of multiple cameras in modern smartphones, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the SR of the lesser zoomed (short-focus) image. Furthermore, SelfDZSR learns a deep network to obtain the SR result of short-focus image to have the same resolution as the telephoto image. For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch. To mitigate the effect of the misalignment between short-focus low-resolution (LR) image and telephoto ground-truth (GT) image, we design an auxiliary-LR generator and map the GT to an auxiliary-LR while keeping the spatial position unchanged. Then the auxiliary-LR can

Author

Zhang, Zhilu; Wang, Ruohao; Zhang, Hongzhi; Chen, Yunjin; Zuo, Wangmeng

Published

2022

Language

EN