72 citations · 446 across the 40 of their papers we have counts for
14 papers · 1 filter
BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization
Yixin Yang, Zhongzheng Peng, Xiaoyu Du +3
How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an…
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring
Lingshun Kong, Jiangxin Dong, Mingqiang Li +2
We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the…
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Maurizio Denna +93
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
Haoran Bai, Di Kang, Haoxian Zhang +2
We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions,…
SLLEN: Semantic-aware Low-light Image Enhancement Network
Mingye Ju, Chuheng Chen, Charles A. Guo +3
How to effectively explore semantic feature is vital for low-light image enhancement (LLE). Existing methods usually utilize the semantic feature that is only drawn from the output…
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement
Cong Wang, Jinshan Pan, Xiao-Ming Wu
We propose an effective Structural Prior guided Generative Adversarial Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly contains a generator with two disc…
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
Hao Li, Zhijing Yang, Xiaobin Hong +4
Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs. Recently, the Vision Transformer (ViT) has exhib…
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…
Real-World Image Super-Resolution by Exclusionary Dual-Learning
Hao Li, Jinghui Qin, Zhijing Yang +4
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attent…
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Long Sun, Jinshan Pan, Jinhui Tang
Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, f…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
Online-updated High-order Collaborative Networks for Single Image Deraining
Cong Wang, Jinshan Pan, Xiao-Ming Wu
Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of th…
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Man Zhou, Keyu Yan, Jinshan Pan +3
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance…
Self-Supervised Deep Blind Video Super-Resolution
Haoran Bai, Jinshan Pan
Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring op…