961 citations · 1.5k across the 30 of their papers we have counts for
31 papers
BasicAVSR: Arbitrary-Scale Video Super-Resolution via Image Priors and Enhanced Motion Compensation
Wei Shang, Wanying Zhang, Shuhang Gu +3
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video frames, potentially at various scaling factors, which presents several challenges regarding sp…
Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Wei Shang, Dongwei Ren, Wanying Zhang +3
Local motion blur in digital images originates from the relative motion between dynamic objects and static imaging systems during exposure. Existing deblurring methods face signifi…
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
Wei Shang, Dongwei Ren, Wanying Zhang +3
The primary challenge in accelerating image super-resolution lies in reducing computation while maintaining performance and adaptability. Motivated by the observation that high-fre…
Efficient RAW Image Deblurring with Adaptive Frequency Modulation
Wenlong Jiao, Binglong Li, Wei Shang +2
Image deblurring plays a crucial role in enhancing visual clarity across various applications. Although most deep learning approaches primarily focus on sRGB images, which inherent…
Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
Tianyi Zhu, Dongwei Ren, Qilong Wang +2
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, g…
Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
Dongwei Ren, Xinya Shu, Yu Li +3
For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map…
Thin-Plate Spline-based Interpolation for Animation Line Inbetweening
Tianyi Zhu, Wei Shang, Dongwei Ren +1
Animation line inbetweening is a crucial step in animation production aimed at enhancing animation fluidity by predicting intermediate line arts between two key frames. However, ex…
SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation
Wei Shang, Dongwei Ren, Wanying Zhang +3
Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning wit…
Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
Wei Shang, Dongwei Ren, Wanying Zhang +3
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video frames, potentially at various scaling factors, which presents several challenges regarding sp…
Improving Image Restoration through Removing Degradations in Textual Representations
Jingbo Lin, Zhilu Zhang, Yuxiang Wei +3
In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restora…
Aggregating Nearest Sharp Features via Hybrid Transformers for Video Deblurring
Wei Shang, Dongwei Ren, Yi Yang +1
Video deblurring methods, aiming at recovering consecutive sharp frames from a given blurry video, usually assume that the input video suffers from consecutively blurry frames. How…
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
Wei Shang, Dongwei Ren, Chaoyu Feng +3
Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To c…
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Wei Shang, Dongwei Ren, Yi Yang +3
Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and le…
Learning Single Image Defocus Deblurring with Misaligned Training Pairs
Yu Li, Dongwei Ren, Xinya Shu +1
By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on well aligned training image pairs. Although training pairs of ground-truth and blurry i…
Robust Deep Ensemble Method for Real-world Image Denoising
Pengju Liu, Hongzhi Zhang, Jinghui Wang +3
Recently, deep learning-based image denoising methods have achieved promising performance on test data with the same distribution as training set, where various denoising models ba…
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Meisong Zheng +75
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the…
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Yue Cao, Zhaolin Wan, Dongwei Ren +2
Full-reference (FR) image quality assessment (IQA) evaluates the visual quality of a distorted image by measuring its perceptual difference with pristine-quality reference, and has…
Learning Dual-Pixel Alignment for Defocus Deblurring
Yu Li, Yaling Yi, Dongwei Ren +2
It is a challenging task to recover sharp image from a single defocus blurry image in real-world applications. On many modern cameras, dual-pixel (DP) sensors create two-image view…
Localization Distillation for Object Detection
Zhaohui Zheng, Rongguang Ye, Qibin Hou +4
Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling…
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
Chaohao Xie, Dongwei Ren, Lei Wang +1
Recently, several weakly supervised learning methods have been devoted to utilize bounding box supervision for training deep semantic segmentation models. Most existing methods usu…
Localization Distillation for Dense Object Detection
Zhaohui Zheng, Rongguang Ye, Ping Wang +4
Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating…
Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
Yu Li, Ming Liu, Yaling Yi +3
Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical application scenarios. For improving reflection remov…
Unpaired Learning of Deep Image Denoising
Xiaohe Wu, Ming Liu, Yue Cao +2
We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Such problem setting generally is practical and valuable consider…
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
Zhaohui Zheng, Ping Wang, Dongwei Ren +4
Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancin…