32 citations · 142 across the 16 of their papers we have counts for
6 papers · 1 filter
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network
Kwanyoung Kim, Se Young Chun
Deep learning based single image super-resolution (SR) methods have been rapidly evolved over the past few years and have yielded state-of-the-art performances over conventional me…
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module
Dongwon Park, Yonghyeok Seo, Se Young Chun
Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp…
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images
Dongwon Park, Yonghyeok Seo, Se Young Chun
Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to…
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior
Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Compressive sensing is a method to recover the original image from undersampled measurements. In order to overcome the ill-posedness of this inverse problem, image priors are used…
Classification based Grasp Detection using Spatial Transformer Network
Dongwon Park, Se Young Chun
Robotic grasp detection task is still challenging, particularly for novel objects. With the recent advance of deep learning, there have been several works on detecting robotic gras…
Training Deep Learning Based Denoisers without Ground Truth Data
Shakarim Soltanayev, Se Young Chun
Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D. They are typically trained to minimize the mean squared…