Proceedings ArticleDOI
NTIRE 2019 Challenge on Image Enhancement: Methods and Results
Andrey Ignatov,Radu Timofte,Xiaochao Qu,Xingguang Zhou,Ting Liu,Pengfei Wan,Syed Waqas Zamir,Aditya Arora,Salman Khan,Fahad Shahbaz Khan,Ling Shao,Dongwon Park,Se Young Chun,Pablo Navarrete Michelini,Hanwen Liu,Dan Zhu,Zhiwei Zhong,Xianming Liu,Junjun Jiang,Debin Zhao,Muhammad Haris,Kazutoshi Akita,Tomoki Yoshida,Greg Shakhnarovich,Norimichi Ukita,Jie Liu,Cheolkon Jung,Raimondo Schettini,Simone Bianco,Claudio Cusano,Flavio Piccoli,Pengju Liu,Kai Zhang,Jingdong Liu,Jiye Liu,Hongzhi Zhang,Wangmeng Zuo,Nelson Chong Ngee Bow,Lai-Kuan Wong,John See,Jinghui Qin,Lishan Huang,Yukai Shi,Pengxu Wei,Wushao Wen,Liang Lin,Zheng Hui,Xiumei Wang,Xinbo Gao,Kanti Kumari,Vikas Kumar Anand,Mahendra Khened,Ganapathy Krishnamurthi +52 more
- pp 2224-2232
TLDR
The first NTIRE challenge on perceptual image enhancement as discussed by the authors focused on proposed solutions and results of real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera.Abstract:
This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image denoising, image resolution and sharpness enhancement, image color/contrast/exposure adjustment, etc. The target metric used in this challenge combined PSNR and SSIM scores with solutions' perceptual results measured in the user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for practical image enhancement.read more
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Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
TL;DR: The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
Journal ArticleDOI
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
TL;DR: The High-Resolution Network (HRNet) as mentioned in this paper maintains high-resolution representations through the whole process by connecting the high-to-low resolution convolution streams in parallel and repeatedly exchanging the information across resolutions.
Proceedings ArticleDOI
Restormer: Efficient Transformer for High-Resolution Image Restoration
TL;DR: Restormer as discussed by the authors proposes an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.
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AI Benchmark: All About Deep Learning on Smartphones in 2019
Andrey Ignatov,Radu Timofte,Andrei Kulik,Seung-Soo Yang,Ke Wang,Felix Baum,Max Wu,Lirong Xu,Luc Van Gool +8 more
TL;DR: This paper evaluates the performance and compares the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference and discusses the recent changes in the Android ML pipeline.
Proceedings ArticleDOI
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Radu Timofte,Maurizio Denna,Abdel Younes,Andrew Lek,Mustafa Ayazoglu,Jie Liu,Zongcai Du,Jiaming Guo,Xueyi Zhou,Hao Jia,Youliang Yan,Zexin Zhang,Yixin Chen,Yunbo Peng,Yue Lin,Xindong Zhang,Hui Zeng,Kun Zeng,Peirong Li,Zhihuang Liu,Shiqi Xue,Shengpeng Wang +22 more
TL;DR: In this paper, the authors introduced the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a realtime performance on mobile or edge NPUs.
References
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