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20172023
most citedToward Real-World Super-Resolution via Adaptive Downsampling Models

48 citations · 78 across the 8 of their papers we have counts for

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10 papers · 1 filter

cs.CV20229 cited

CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image

Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son +1

Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs…

cs.CV20224 cited

AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network

Wooseok Lee, Sanghyun Son, Kyoung Mu Lee

Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less prac…

cs.CV20227 cited

C2N: Practical Generative Noise Modeling for Real-World Denoising

Geonwoon Jang, Wooseok Lee, Sanghyun Son +1

Learning-based image denoising methods have been bounded to situations where well-aligned noisy and clean images are given, or samples are synthesized from predetermined noise mode…

cs.CV2021

NTIRE 2021 Challenge on Video Super-Resolution

Sanghyun Son, Suyoung Lee, Seungjun Nah +2

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIR…

cs.CV2021

NTIRE 2021 Challenge on Image Deblurring

Seungjun Nah, Sanghyun Son, Suyoung Lee +2

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge o…

cs.CV20212 cited

SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

Sanghyun Son, Kyoung Mu Lee

Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to…