5 citations · 11 across the 5 of their papers we have counts for
8 papers
Variational Deep Image Denoising
Jae Woong Soh, Nam Ik Cho
Convolutional neural networks (CNNs) have shown outstanding performance on image denoising with the help of large-scale datasets. Earlier methods naively trained a single CNN with…
Deep Universal Blind Image Denoising
Jae Woong Soh, Nam Ik Cho
Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. Traditionally, many researchers…
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization
Yoonsik Kim, Jae Woong Soh, Gu Yong Park +1
Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order…
Meta-Transfer Learning for Zero-Shot Super-Resolution
Jae Woong Soh, Sunwoo Cho, Nam Ik Cho
Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable perfor…
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
Jae Woong Soh, Gu Yong Park, Junho Jo +1
Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objec…
Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network
Junho Jo, Hyung Il Koo, Jae Woong Soh +1
We present a new handwritten text segmentation method by training a convolutional neural network (CNN) in an end-to-end manner. Many conventional methods addressed this problem by…