E
Eunju Cha
Researcher at KAIST
Publications - 17
Citations - 442
Eunju Cha is an academic researcher from KAIST. The author has contributed to research in topics: Deep learning & Iterative reconstruction. The author has an hindex of 5, co-authored 15 publications receiving 331 citations.
Papers
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Journal ArticleDOI
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
TL;DR: Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems.
Journal ArticleDOI
Deep learning STEM-EDX tomography of nanocrystals
Yoseob Han,Yoseob Han,Jaeduck Jang,Eunju Cha,Jun-Ho Lee,Hyungjin Chung,Myoungho Jeong,Tae-Gon Kim,Byeong Gyu Chae,Hee Goo Kim,Shinae Jun,Sungwoo Hwang,Eunha Lee,Jong Chul Ye +13 more
TL;DR: An unsupervised deep learning approach is proposed for high-quality 3D EDX tomography of core–shell nanocrystals, which can be usually permanently dammaged by prolonged electron beam, to reconstruct 3D images and observe the relationship between optical and structural properties of semiconductor nanocry crystals, of interest in optoelectronic applications.
Journal ArticleDOI
Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution
TL;DR: A novel unpaired training scheme for deep learning using optimal transport driven cycle-consistent generative adversarial network (cycleGAN) with just a single pair of generator and discriminator is proposed, which makes the training much simpler but still improves the performance.
Journal ArticleDOI
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
TL;DR: A systematic geometric approach using bootstrapping and subnetwork aggregation using an attention module to increase the expressivity of the underlying neural network to improve reconstruction performance with negligible complexity increases is proposed.
Journal ArticleDOI
Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images
Mi-Sun Kang,Eunju Cha,Eun Hee Kang,Jong Chul Ye,Nam-Gu Her,Jeong-Woo Oh,Do-Hyun Nam,Myoung-Hee Kim,Sejung Yang +8 more
TL;DR: A novel method to improve quantification accuracy using a super-resolution with a convolutional neural network (CNN) with image-based cell phenotypic profiling to predict the responses of glioblastoma cells to a drug using automatic image processing is proposed.