Search Results for author: Sung-Jea Ko

Found 12 papers, 4 papers with code

Image Generation with Self Pixel-wise Normalization

no code implementations26 Jan 2022 Yoon-Jae Yeo, Min-Cheol Sagong, Seung Park, Sung-Jea Ko, Yong-Goo Shin

Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique.

Image-to-Image Translation

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

4 code implementations ICCV 2021 Sung-Jin Cho, Seo-won Ji, Jun-Pyo Hong, Seung-Won Jung, Sung-Jea Ko

Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.

Ranked #5 on Deblurring on RealBlur-J (using extra training data)

Deblurring Image Deblurring

W-Net: Two-stage U-Net with misaligned data for raw-to-RGB mapping

1 code implementation20 Nov 2019 Kwang-Hyun Uhm, Seung-Wook Kim, Seo-won Ji, Sung-Jin Cho, Jun-Pyo Hong, Sung-Jea Ko

Recent research on learning a mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural networks.

Simple yet Effective Way for Improving the Performance of GAN

no code implementations19 Nov 2019 Yong-Goo Shin, Yoon-Jae Yeo, Sung-Jea Ko

In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features.

Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations

no code implementations18 Nov 2019 Cheol-hwan Yoo, Seo-won Ji, Yong-Goo Shin, Seung-Wook Kim, Sung-Jea Ko

In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently.

3D Hand Pose Estimation

cGANs with Conditional Convolution Layer

no code implementations3 Jun 2019 Min-Cheol Sagong, Yong-Goo Shin, Yoon-Jae Yeo, Seung Park, Sung-Jea Ko

Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator.

Conditional Image Generation

PEPSI++: Fast and Lightweight Network for Image Inpainting

no code implementations22 May 2019 Yong-Goo Shin, Min-Cheol Sagong, Yoon-Jae Yeo, Seung-Wook Kim, Sung-Jea Ko

To address this problem, we propose a novel network architecture called PEPSI: parallel extended-decoder path for semantic inpainting network, which aims at reducing the hardware costs and improving the inpainting performance.

Image Inpainting SSIM

Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays

no code implementations15 May 2019 Yong-Goo Shin, Seung Park, Yoon-Jae Yeo, Min-Jae Yoo, Sung-Jea Ko

In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN).

Image Quality Assessment

Parallel Feature Pyramid Network for Object Detection

3 code implementations ECCV 2018 Seung-Wook Kim, Hyong-Keun Kook, Jee-Young Sun, Mun-Cheon Kang, Sung-Jea Ko

To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth.

object-detection Object Detection

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