Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

no code implementations • 19 Nov 2022 • Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou, Yu Liu, Qixiang Pang, Shan Du

First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.

Generative Adversarial Network Image Super-Resolution +1

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

no code implementations • 18 Nov 2022 • Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du

In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).

Image Super-Resolution

Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition

no code implementations • 26 Sep 2022 • Xinnan Ding, Shan Du, Yu Zhang, Kejun Wang

The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences.

Gait Recognition Optical Flow Estimation