46 citations · 90 across the 10 of their papers we have counts for
10 papers
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
Renlong Wu, Zhilu Zhang, Shuohao Zhang +2
Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the im…
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…
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…
On Steering Multi-Annotations per Sample for Multi-Task Learning
Yuanze Li, Yiwen Guo, Qizhang Li +2
The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously r…
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
Zhilu Zhang, Ruohao Wang, Hongzhi Zhang +2
In this paper, we consider two challenging issues in reference-based super-resolution (RefSR), (i) how to choose a proper reference image, and (ii) how to learn real-world RefSR in…
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
Zhaoyi Yan, Ruimao Zhang, Hongzhi Zhang +2
Crowd counting is critical for numerous video surveillance scenarios. One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by th…
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
Jiazhi Du, Xin Qiao, Zifei Yan +2
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model. Ho…
Multi-level Wavelet Convolutional Neural Networks
Pengju Liu, Hongzhi Zhang, Wei Lian +1
In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can caus…
Multi-level Wavelet-CNN for Image Restoration
Pengju Liu, Hongzhi Zhang, Kai Zhang +2
The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the ex…
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Ibrahim Omara, Hongzhi Zhang, Faqiang Wang +1
Ear recognition task is known as predicting whether two ear images belong to the same person or not. In this paper, we present a novel metric learning method for ear recognition. T…