3 citations · 9 across the 8 of their papers we have counts for
8 papers
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution
Yong Liu, Hang Dong, Jinshan Pan +5
While diffusion models significantly improve the perceptual quality of super-resolved images, they usually require a large number of sampling steps, resulting in high computational…
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Yong Liu, Hang Dong, Boyang Liang +6
Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increa…
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
Yuhao Huang, Hang Dong, Jinshan Pan +5
The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have di…
MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion
Jing Wang, Haotian Fan, Xiaoxia Hou +4
Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from imag…
Residual Local Feature Network for Efficient Super-Resolution
Fangyuan Kong, Mingxi Li, Songwei Liu +5
Deep learning based approaches has achieved great performance in single image super-resolution (SISR). However, recent advances in efficient super-resolution focus on reducing the…
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
Zongcai Du, Ding Liu, Jie Liu +3
Runtime and memory consumption are two important aspects for efficient image super-resolution (EISR) models to be deployed on resource-constrained devices. Recent advances in EISR…
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
Jingwen He, Wu Shi, Kai Chen +2
Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impress…
Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring
Chao Zhu, Hang Dong, Jinshan Pan +4
The success of the state-of-the-art video deblurring methods stems mainly from implicit or explicit estimation of alignment among the adjacent frames for latent video restoration.…