72 citations · 446 across the 40 of their papers we have counts for
37 papers · 1 filter
Towards Unified Deep Image Deraining: A Survey and A New Benchmark
Xiang Chen, Jinshan Pan, Jiangxin Dong +1
Recent years have witnessed significant advances in image deraining due to the kinds of effective image priors and deep learning models. As each deraining approach has individual s…
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
Liang Chen, Jiawei Zhang, Zhenhua Li +4
Images taken under the low-light condition often contain blur and saturated pixels at the same time. Deblurring images with saturated pixels is quite challenging. Because of the li…
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
Liyan Wang, Qinyu Yang, Cong Wang +3
Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper de…
Learning A Sparse Transformer Network for Effective Image Deraining
Xiang Chen, Hao Li, Mingqiang Li +1
Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction…
Open-World Pose Transfer via Sequential Test-Time Adaption
Junyang Chen, Xiaoyu Xian, Zhijing Yang +5
Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representat…
SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency
Cong Wang, Jinshan Pan, Wanyu Lin +2
This work presents an effective depth-consistency self-prompt Transformer for image dehazing. It is motivated by an observation that the estimated depths of an image with haze resi…
Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution
Long Sun, Jiangxin Dong, Jinhui Tang +1
Although numerous solutions have been proposed for image super-resolution, they are usually incompatible with low-power devices with many computational and memory constraints. In t…
Deep Dynamic Scene Deblurring from Optical Flow
Jiawei Zhang, Jinshan Pan, Daoye Wang +5
Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as trackin…
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution
Xiang Li, Jinshan Pan, Jinhui Tang +1
We propose an effective lightweight dynamic local and global self-attention network (DLGSANet) to solve image super-resolution. Our method explores the properties of Transformers w…
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…
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…
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…
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien +1
Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms…
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Xiang Chen, Jinshan Pan, Kui Jiang +5
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible. Ho…
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
Hang Dong, Jinshan Pan, Lei Xiang +4
In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles,…