no code implementations • 16 Mar 2023 • Xin Qiao, Chenyang Ge, Youmin Zhang, Yanhui Zhou, Fabio Tosi, Matteo Poggi, Stefano Mattoccia
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features.
no code implementations • 28 Sep 2022 • Hao Wei, Chenyang Ge, Xin Qiao, Pengchao Deng
In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network architecture design.
1 code implementation • 24 Jun 2020 • Jiazhi Du, Xin Qiao, Zifei Yan, Hongzhi Zhang, WangMeng Zuo
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.