1 citations · 2 across the 8 of their papers we have counts for
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MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
Zhicun Yin, Ming Liu, Xiaoming Li +3
Due to their highly structured characteristics, faces are easier to recover than natural scenes for blind image super-resolution. Therefore, we can extract the degradation represen…
Automatic Localization and Detection Applicable to Robust Image Watermarking Resisting against Camera Shooting
Ming Liu
Robust image watermarking that can resist camera shooting has become an active research topic in recent years due to the increasing demand for preventing sensitive information disp…
Human Guided Ground-truth Generation for Realistic Image Super-resolution
Du Chen, Jie Liang, Xindong Zhang +3
How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resoluti…
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Ming Liu, Yuxiang Wei, Xiaohe Wu +2
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality. With the ability to g…
Learning Diverse Tone Styles for Image Retouching
Haolin Wang, Jiawei Zhang, Ming Liu +2
Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing…
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Xiaomin Wang +3
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.…