activity
20182021
most citedDual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

17 citations · 24 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20214 cited

Pushing the Envelope of Thin Crack Detection

Liang Xu, Taro Hatsutani, Xing Liu +3

In this study, we consider the problem of detecting cracks from the image of a concrete surface for automated inspection of infrastructure, such as bridges. Its overall accuracy is…

eess.IV20193 cited

Unsupervised Image Super-Resolution with an Indirect Supervised Path

Zhen Han, Enyan Dai, Xu Jia +5

The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image. Although significant progress has been made b…

cs.CV2019

Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network

Xing Liu, Masanori Suganuma, Xiyang Luo +1

The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors. However, high-performa…

cs.LG2019

Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences

Xing Liu, Takayuki Okatani

Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However…

cs.CV201917 cited

Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

Xing Liu, Masanori Suganuma, Zhun Sun +1

In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exp…

cs.CV2018

Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions

Masanori Suganuma, Xing Liu, Takayuki Okatani

Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. There are many different types of distortion which…