23 citations · 47 across the 7 of their papers we have counts for
8 papers
InFusionSurf: Refining Neural RGB-D Surface Reconstruction Using Per-Frame Intrinsic Refinement and TSDF Fusion Prior Learning
Seunghwan Lee, Gwanmo Park, Hyewon Son +2
We introduce InFusionSurf, an innovative enhancement for neural radiance field (NeRF) frameworks in 3D surface reconstruction using RGB-D video frames. Building upon previous metho…
NoiseTransfer: Image Noise Generation with Contrastive Embeddings
Seunghwan Lee, Tae Hyun Kim
Deep image denoising networks have achieved impressive success with the help of a considerably large number of synthetic train datasets. However, real-world denoising is a still ch…
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
Han Joo Chae, Seunghwan Lee, Hyewon Son +2
We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers duri…
Cycled Compositional Learning between Images and Text
Jongseok Kim, Youngjae Yu, Seunghwan Lee +1
We present an approach named the Cycled Composition Network that can measure the semantic distance of the composition of image-text embedding. First, the Composition Network transi…
CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data
Youngjae Yu, Seunghwan Lee, Yuncheol Choi +1
We present an approach named CurlingNet that can measure the semantic distance of composition of image-text embedding. In order to learn an effective image-text composition for the…
Restore from Restored: Single Image Denoising with Pseudo Clean Image
Seunghwan Lee, Dongkyu Lee, Donghyeon Cho +2
In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoisin…
Restore from Restored: Video Restoration with Pseudo Clean Video
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during…
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and thes…