32 citations · 142 across the 16 of their papers we have counts for
17 papers
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
Tao Hong, Zhaoyi Xu, Se Young Chun +2
In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prio…
Neural Diffeomorphic Non-uniform B-spline Flows
Seongmin Hong, Se Young Chun
Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applica…
Rethinking Deep Image Prior for Denoising
Yeonsik Jo, Se Young Chun, Jonghyun Choi
Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting…
Image Restoration by Deep Projected GSURE
Shady Abu-Hussein, Tom Tirer, Se Young Chun +2
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Ne…
Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring
Dongwon Park, Dong Un Kang, Se Young Chun
One of the key components for video deblurring is how to exploit neighboring frames. Recent state-of-the-art methods either used aligned adjacent frames to the center frame or prop…
Task-Aware Variational Adversarial Active Learning
Kwanyoung Kim, Dongwon Park, Kwang In Kim +1
Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques. Active learning (AL) tackles this by quer…
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
Dongwon Park, Dong Un Kang, Jisoo Kim +1
Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then…
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection
Dongwon Park, Yonghyeok Seo, Dongju Shin +2
Recently, robotic grasp detection (GD) and object detection (OD) with reasoning have been investigated using deep neural networks (DNNs). There have been works to combine these mul…
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Dongwon Park, Jisoo Kim, Se Young Chun
Multi-scale approach has been used for blind image / video deblurring problems to yield excellent performance for both conventional and recent deep-learning-based state-of-the-art…
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training of deep neural network Gaussian denoisers that outperformed classical non-deep learning b…
Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network For Inverse Problems In Image Processing
Byung Hyun Lee, Se Young Chun
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimizat…
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…