961 citations · 1k across the 4 of their papers we have counts for
7 papers
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…
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Qilong Wang, Li Zhang, Banggu Wu +4
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification tas…
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zhaohui Zheng, Ping Wang, Wei Liu +3
Bounding box regression is the crucial step in object detection. In existing methods, while -norm loss is widely adopted for bounding box regression, it is not tailored to…
Neural Blind Deconvolution Using Deep Priors
Dongwei Ren, Kai Zhang, Qilong Wang +2
Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on f…
STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren +5
Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives a…
Progressive Image Deraining Networks: A Better and Simpler Baseline
Dongwei Ren, Wangmeng Zuo, Qinghua Hu +2
Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the co…