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20172022
most citedForget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

40 citations · 114 across the 15 of their papers we have counts for

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Showing 2020 · cs.CVShow all

5 papers · 2 filters

cs.CV2020

Zoom-to-Inpaint: Image Inpainting with High-Frequency Details

Soo Ye Kim, Kfir Aberman, Nori Kanazawa +6

Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propo…

cs.CV2020★ 6 cited

KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

Soo Ye Kim, Hyeonjun Sim, Munchurl Kim

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contai…

cs.CV2020★ 40 cited

Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

Juan Luis Gonzalez, Munchurl Kim

Self-supervised depth estimators have recently shown results comparable to the supervised methods on the challenging single image depth estimation (SIDE) task, by exploiting the ge…

cs.CV2020

NTIRE 2020 Challenge on NonHomogeneous Dehazing

Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu +49

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results e…

cs.CV2020★ 1 cited

AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

Seungjun Nah, Sanghyun Son, Radu Timofte +1

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challeng…