most citedRethinking Coarse-to-Fine Approach in Single Image Deblurring

34 citations · 37 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV202134 cited

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong +2

Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks. Conventional methods typically stack sub-networks with multi-s…

eess.IV20193 cited

AIM 2019 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Gregory Slabaugh +25

This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper desc…

cs.LG2019

Simple yet Effective Way for Improving the Performance of GAN

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.…

cs.CV2019

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

Cheol-hwan Yoo, Seo-won Ji, Yong-goo Shin +2

3D hand pose estimation from a single depth image plays an important role in computer vision and human-computer interaction. Although recent hand pose estimation methods using conv…

eess.IV2019

Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays

Yong-Goo Shin, Seung Park, Yoon-Jae Yeo +2

Various power-constrained contrast enhancement (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display w…