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Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer
Agus Gunawan;Soo Ye Kim;Hyeonjun Sim;Jae-Ho Lee;Munchurl Kiml
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via ph...Show More
ObjectStitch: Object Compositing with Diffusion Model
Yizhi Song;Zhifei Zhang;Zhe Lin;Scott Cohen;Brian Price;Jianming Zhang;Soo Ye Kim;Daniel Aliaga
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (12)
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generat...Show More
Layered Depth Refinement with Mask Guidance
Soo Ye Kim;Jianming Zhang;Simon Niklaus;Yifei Fan;Simon Chen;Zhe Lin;Munchurl Kim
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation ...Show More
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Soo Ye Kim;Kfir Aberman;Nori Kanazawa;Rahul Garg;Neal Wadhwa;Huiwen Chang;Nikhil Karnad;Munchurl Kim;Orly Liba
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (12)
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 propose applying super-resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. By introducing high-resolution images to the refinement networ...Show More
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (49)
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 contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it ...Show More
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (69)
Recent modern displays are now able to render high dynamic range (HDR), high resolution (HR) videos of up to 8K UHD (Ultra High Definition). Consequently, UHD HDR broadcasting and streaming have emerged as high quality premium services. However, due to the lack of original UHD HDR video content, appropriate conversion technologies are urgently needed to transform the legacy low resolution (LR) sta...Show More
Video Super-Resolution Based on 3D-CNNS with Consideration of Scene Change
2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (39)
In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for the accurate prediction of the high resolution frames. Although 2D-CNNs are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve the temporal information. To this end, we propose an effective 3D-CNN for video super...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
Year: 2018 | Conference Paper |
High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes
Hyeonjun Sim;Sehwan Ki;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (18)
Haze removal is one of the essential image enhancement processes that makes degraded images visually pleasing. Since haze in images often appears differently depending on the surroundings, haze removal requires the use of spatial information to effectively remove the haze according to the types of image region characteristics. However, in the conventional training, the small-sized training image p...Show More
Fully End-to-End Learning Based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing
Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (21)
A receptive field is defined as the region in an input image space that an output image pixel is looking at. Thus, the receptive field size influences the learning of deep convolution neural networks. Especially, in single image dehazing problems, larger receptive fields often show more effective dehazying by considering the brightness and color of the entire input hazy image without additional in...Show More
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (183)
This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were lear...Show More
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu
Year: 2018 | Conference Paper |
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