Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks.
In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject.
In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds.
Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects.
We claim that the performance of inpainting algorithms can be better judged by the generated structures and textures.
Inspired by this workflow, we propose a new learning task of automatic segmentation of inpainting perceptual artifacts, and apply the model for inpainting model evaluation and iterative refinement.
Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge.
Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions.
NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation.
Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image.
Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases.
Ranked #1 on Retinal Vessel Segmentation on CHASE_DB1
no code implementations • 18 Aug 2020 • Yuqian Zhou, Michael Kwan, Kyle Tolentino, Neil Emerton, Sehoon Lim, Tim Large, Lijiang Fu, Zhihong Pan, Baopu Li, Qirui Yang, Yihao Liu, Jigang Tang, Tao Ku, Shibin Ma, Bingnan Hu, Jiarong Wang, Densen Puthussery, Hrishikesh P. S, Melvin Kuriakose, Jiji C. V, Varun Sundar, Sumanth Hegde, Divya Kothandaraman, Kaushik Mitra, Akashdeep Jassal, Nisarg A. Shah, Sabari Nathan, Nagat Abdalla Esiad Rahel, Dafan Chen, Shichao Nie, Shuting Yin, Chengconghui Ma, Haoran Wang, Tongtong Zhao, Shanshan Zhao, Joshua Rego, Huaijin Chen, Shuai Li, Zhenhua Hu, Kin Wai Lau, Lai-Man Po, Dahai Yu, Yasar Abbas Ur Rehman, Yiqun Li, Lianping Xing
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image.
Ranked #7 on Image Super-Resolution on Manga109 - 3x upscaling
1 code implementation • 8 May 2020 • Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S. Brown, Yue Cao, Zhilu Zhang, WangMeng Zuo, Xiaoling Zhang, Jiye Liu, Wendong Chen, Changyuan Wen, Meng Liu, Shuailin Lv, Yunchao Zhang, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Xiyu Yu, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Songhyun Yu, Bumjun Park, Jechang Jeong, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Zengli Yang, Long Bao, Shuangquan Wang, Dongwoon Bai, Jungwon Lee, Youngjung Kim, Kyeongha Rho, Changyeop Shin, Sungho Kim, Pengliang Tang, Yiyun Zhao, Yuqian Zhou, Yuchen Fan, Thomas Huang, Zhihao LI, Nisarg A. Shah, Wei Liu, Qiong Yan, Yuzhi Zhao, Marcin Możejko, Tomasz Latkowski, Lukasz Treszczotko, Michał Szafraniuk, Krzysztof Trojanowski, Yanhong Wu, Pablo Navarrete Michelini, Fengshuo Hu, Yunhua Lu, Sujin Kim, Wonjin Kim, Jaayeon Lee, Jang-Hwan Choi, Magauiya Zhussip, Azamat Khassenov, Jong Hyun Kim, Hwechul Cho, Priya Kansal, Sabari Nathan, Zhangyu Ye, Xiwen Lu, Yaqi Wu, Jiangxin Yang, Yanlong Cao, Siliang Tang, Yanpeng Cao, Matteo Maggioni, Ioannis Marras, Thomas Tanay, Gregory Slabaugh, Youliang Yan, Myungjoo Kang, Han-Soo Choi, Kyungmin Song, Shusong Xu, Xiaomu Lu, Tingniao Wang, Chunxia Lei, Bin Liu, Rajat Gupta, Vineet Kumar
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
In this paper, We present a landmark driven two-stream network to generate faithful talking facial animation, in which more facial details are created, preserved and transferred from multiple source images instead of a single one.
Spatial-temporal feature learning is of vital importance for video emotion recognition.
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN.
Ranked #2 on Denoising on Darmstadt Noise Dataset
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling.
Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i. e. the number of independent identities from the target domain is unknown).
The integration of information across multiple modalities and across time is a promising way to enhance the emotion recognition performance of affective systems.
The newly proposed RepGAN is tested on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised classification, generation and reconstruction tasks.
However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems.
Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing.
Ranked #55 on Person Re-Identification on DukeMTMC-reID
It handles the problem of disambiguating changes due to identity and changes due to facial expression by learning to generate the difference between low-level features of images of the same person but with different facial expressions.