AIM 2020 Challenge on Rendering Realistic Bokeh
Andrey Ignatov,Radu Timofte,Ming Qian,Congyu Qiao,Jiamin Lin,Zhenyu Guo,Chenghua Li,Cong Leng,Jian Cheng,Juewen Peng,Xianrui Luo,Ke Xian,Zijin Wu,Zhiguo Cao,Densen Puthussery,C. V. Jiji,P. S. Hrishikesh,Melvin Kuriakose,Saikat Dutta,Sourya Dipta Das,Nisarg A. Shah,Kuldeep Purohit,Praveen Kandula,Maitreya Suin,A. N. Rajagopalan,M. B. Saagara,A. L. Minnu,A. R. Sanjana,S. Praseeda,Ge Wu,Xueqin Chen,Tengyao Wang,Max Zheng,Hulk Wong,Jay Zou +34 more
- pp 213-228
TLDR
The second AIM realistic bokeh effect rendering challenge as discussed by the authors was the first attempt to learn a realistic shallow focus technique using a large-scale EBB! dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera.Abstract:
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.read more
Citations
More filters
Posted Content
Efficient Image Super-Resolution Using Pixel Attention
TL;DR: This work designs a lightweight convolutional neural network for image super resolution with a newly proposed pixel attention scheme that could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters.
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.
Proceedings ArticleDOI
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Andrés Romero,Heewon Kim,Radu Timofte,Chiu Man Ho,Zibo Meng,Kyoung Mu Lee,Yuxiang Chen,Yutong Wang,Zeyu Long,Chenhao Wang,Yifei Chen,Boshen Xu,Shuhang Gu,Lixin Duan,Wen Li,Wang Bofei,Zhang Diankai,Zheng Chengjian,Liu Shaoli,Gao Si,Zhang Xiaofeng,Lu Kaidi,Xu Tianyu,Zheng Hui,Xinbo Gao,Xiumei Wang,Jiaming Guo,Xueyi Zhou,Hao Jia,Youliang Yan +30 more
TL;DR: In this paper, the first Mobile AI challenge was introduced, where the target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a real-time performance on mobile GPUs.
Proceedings ArticleDOI
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Cheng-Ming Chiang,Hsien-Kai Kuo,Anastasia Sycheva,Radu Timofte,Min-Hung Chen,Man-Yu Lee,Yu-Syuan Xu,Yu Tseng,Shusong Xu,Jin Guo,Chao-Hung Chen,Ming-Chun Hsyu,Wen-Chia Tsai,Chao-Wei Chen,Grigory Malivenko,Minsu Kwon,Myungje Lee,Jaeyoon Yoo,Changbeom Kang,Shinjo Wang,Zheng Shaolong,Hao Dejun,Xie Fen,Feng Zhuang,Yipeng Ma,Jingyang Peng,Tao Wang,Fenglong Song,Chih-Chung Hsu,Kwan-Lin Chen,Mei-Hsuang Wu,Vishal Chudasama,Kalpesh Prajapati,Heena Patel,Anjali Sarvaiya,Kishor P. Upla,Kiran B. Raja,Raghavendra Ramachandra,Christoph Busch,Etienne de Stoutz +40 more
TL;DR: In this article, an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs was developed.
Posted Content
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov,Radu Timofte,Zhilu Zhang,Ming Liu,Haolin Wang,Wangmeng Zuo,Jiawei Zhang,Ruimao Zhang,Zhanglin Peng,Sijie Ren,Linhui Dai,Xiaohong Liu,Chengqi Li,Jun Chen,Yuichi Ito,Bhavya Vasudeva,Puneesh Deora,Umapada Pal,Zhenyu Guo,Yu Zhu,Tian Liang,Chenghua Li,Cong Leng,Zhihong Pan,Baopu Li,Byung-Hoon Kim,Joonyoung Song,Jong Chul Ye,JaeHyun Baek,Magauiya Zhussip,Yeskendir Koishekenov,Hwechul Cho Ye,Xin Liu,Xueying Hu,Jun Jiang,Jinwei Gu,Kai Li,Pengliang Tan,Bingxin Hou +38 more
TL;DR: This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
References
More filters
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Journal ArticleDOI
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
Journal ArticleDOI
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
Proceedings ArticleDOI
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
TL;DR: CycleGAN as discussed by the authors learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.