20 citations · 42 across the 11 of their papers we have counts for
11 papers
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
Xiaoyu Liu, Ming Liu, Junyi Li +4
For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such im…
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…
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…
Self-Supervised Image Restoration with Blurry and Noisy Pairs
Zhilu Zhang, Rongjian Xu, Ming Liu +2
When taking photos under an environment with insufficient light, the exposure time and the sensor gain usually require to be carefully chosen to obtain images with satisfying visua…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
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…
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective
Ming Liu, Jianan Pan, Zifei Yan +2
Recently, multiple synthetic and real-world datasets have been built to facilitate the training of deep single image reflection removal (SIRR) models. Meanwhile, diverse testing se…
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Ming Liu, Yukang Ding, Min Xia +4
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…
Learning Symmetry Consistent Deep CNNs for Face Completion
Xiaoming Li, Ming Liu, Jieru Zhu +4
Deep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global…
Learning Warped Guidance for Blind Face Restoration
Xiaoming Li, Ming Liu, Yuting Ye +3
This paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery…