Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Guo, Chunle"'
Autor:
Duan, Zheng-Peng, zhang, Jiawei, Lin, Zheng, Jin, Xin, Zou, Dongqing, Guo, Chunle, Li, Chongyi
Image retouching aims to enhance the visual quality of photos. Considering the different aesthetic preferences of users, the target of retouching is subjective. However, current retouching methods mostly adopt deterministic models, which not only neg
Externí odkaz:
http://arxiv.org/abs/2407.03757
Autor:
Jin, Xin, Guo, Chunle, Li, Xiaoming, Yue, Zongsheng, Li, Chongyi, Zhou, Shangchen, Feng, Ruicheng, Dai, Yuekun, Yang, Peiqing, Loy, Chen Change, Li, Ruoqi, Liu, Chang, Wang, Ziyi, Du, Yao, Yang, Jingjing, Bao, Long, Sun, Heng, Kong, Xiangyu, Xing, Xiaoxia, Wu, Jinlong, Xue, Yuanyang, Park, Hyunhee, Song, Sejun, Kim, Changho, Tan, Jingfan, Luo, Wenhan, Liu, Zikun, Qiao, Mingde, Jiang, Junjun, Jiang, Kui, Xiao, Yao, Sun, Chuyang, Hu, Jinhui, Ruan, Weijian, Dong, Yubo, Chen, Kai, Jo, Hyejeong, Qin, Jiahao, Han, Bingjie, Qin, Pinle, Chai, Rui, Wang, Pengyuan
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data fo
Externí odkaz:
http://arxiv.org/abs/2406.07006
Autor:
Ren, Bin, Li, Yawei, Mehta, Nancy, Timofte, Radu, Yu, Hongyuan, Wan, Cheng, Hong, Yuxin, Han, Bingnan, Wu, Zhuoyuan, Zou, Yajun, Liu, Yuqing, Li, Jizhe, He, Keji, Fan, Chao, Zhang, Heng, Zhang, Xiaolin, Yin, Xuanwu, Zuo, Kunlong, Liao, Bohao, Xia, Peizhe, Peng, Long, Du, Zhibo, Di, Xin, Li, Wangkai, Wang, Yang, Zhai, Wei, Pei, Renjing, Guo, Jiaming, Xu, Songcen, Cao, Yang, Zha, Zhengjun, Wang, Yan, Liu, Yi, Wang, Qing, Zhang, Gang, Zhang, Liou, Zhao, Shijie, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Liu, Xin, Yan, Min, Wang, Qian, Zhou, Menghan, Yan, Yiqiang, Liu, Yixuan, Chan, Wensong, Tang, Dehua, Zhou, Dong, Wang, Li, Tian, Lu, Emad, Barsoum, Jia, Bohan, Qiao, Junbo, Zhou, Yunshuai, Zhang, Yun, Li, Wei, Lin, Shaohui, Zhou, Shenglong, Chen, Binbin, Liao, Jincheng, Zhao, Suiyi, Zhang, Zhao, Wang, Bo, Luo, Yan, Wei, Yanyan, Li, Feng, Wang, Mingshen, Guan, Jinhan, Hu, Dehua, Yu, Jiawei, Xu, Qisheng, Sun, Tao, Lan, Long, Xu, Kele, Lin, Xin, Yue, Jingtong, Yang, Lehan, Du, Shiyi, Qi, Lu, Ren, Chao, Han, Zeyu, Wang, Yuhan, Chen, Chaolin, Li, Haobo, Zheng, Mingjun, Yang, Zhongbao, Song, Lianhong, Yan, Xingzhuo, Fu, Minghan, Zhang, Jingyi, Li, Baiang, Zhu, Qi, Xu, Xiaogang, Guo, Dan, Guo, Chunle, Chen, Jiadi, Long, Huanhuan, Duanmu, Chunjiang, Lei, Xiaoyan, Liu, Jie, Jia, Weilin, Cao, Weifeng, Zhang, Wenlong, Mao, Yanyu, Guo, Ruilong, Zhang, Nihao, Pandey, Manoj, Chernozhukov, Maksym, Le, Giang, Cheng, Shuli, Wang, Hongyuan, Wei, Ziyan, Tang, Qingting, Wang, Liejun, Li, Yongming, Guo, Yanhui, Xu, Hao, Khatami-Rizi, Akram, Mahmoudi-Aznaveh, Ahmad, Hsu, Chih-Chung, Lee, Chia-Ming, Chou, Yi-Shiuan, Joshi, Amogh, Akalwadi, Nikhil, Malagi, Sampada, Yashaswini, Palani, Desai, Chaitra, Tabib, Ramesh Ashok, Patil, Ujwala, Mudenagudi, Uma
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor
Externí odkaz:
http://arxiv.org/abs/2404.10343
With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number of traini
Externí odkaz:
http://arxiv.org/abs/2310.10769
Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail r
Externí odkaz:
http://arxiv.org/abs/2308.11932
Autor:
Zhou, Jingchun, He, Zongxin, Lam, Kin-Man, Wang, Yudong, Zhang, Weishi, Guo, ChunLe, Li, Chongyi
In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection a
Externí odkaz:
http://arxiv.org/abs/2308.11918
Autor:
Jin, Xin, Xiao, Jia-Wen, Han, Ling-Hao, Guo, Chunle, Liu, Xialei, Li, Chongyi, Cheng, Ming-Ming
Explicit calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods are impeded by several critical limitations: a) the explicit calibration process is both labor- and time-intensive, b
Externí odkaz:
http://arxiv.org/abs/2308.03448
Autor:
Zhou, Man, Zheng, Naishan, Huang, Jie, Rui, Xiangyu, Guo, Chunle, Meng, Deyu, Li, Chongyi, Gu, Jinwei
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint
Externí odkaz:
http://arxiv.org/abs/2303.16438
Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while s
Externí odkaz:
http://arxiv.org/abs/2303.16411
In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global attention, simply concatenate features, or implicitly manipulate featur
Externí odkaz:
http://arxiv.org/abs/2212.05855