activity
20182022
most citedForget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

40 citations · 96 across the 13 of their papers we have counts for

collaborators

27 papers

eess.IV20229 cited

Selective compression learning of latent representations for variable-rate image compression

Jooyoung Lee, Seyoon Jeong, Munchurl Kim

Recently, many neural network-based image compression methods have shown promising results superior to the existing tool-based conventional codecs. However, most of them are often…

eess.IV2022

Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov, Radu Timofte, Jin Zhang +21

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI…

cs.CV20223 cited

Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos

Juan Luis Gonzalez Bello, Jaeho Moon, Munchurl Kim

Recently, much attention has been drawn to learning the underlying 3D structures of a scene from monocular videos in a fully self-supervised fashion. One of the most challenging as…

cs.CV2021

SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery

Jaehyup Lee, Soomin Seo, Munchurl Kim

Pan-sharpening is a process of merging a high-resolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pa…

cs.CV20213 cited

Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images

Dac Tung Vu, Juan Luis Gonzalez, Munchurl Kim

Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog, which can dramatically degrade the quality of captured images. Most existing…

cs.CV2021

PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier

Jihyong Oh, Munchurl Kim

Although Generative Adversarial Networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task mostly due to…