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Explorable Tone Mapping Operators
Chien-Chuan Su,
Ren Wang,
Hung-Jin Lin,
Yu-Lun Liu,
Chia-Ping Chen,
Yu-Lin Chang,
Soo-Chang Pei
Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 2020  
arXiv
In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity.
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Learning Camera-Aware Noise Models
Ke-Chi Chang,
Ren Wang,
Hung-Jin Lin,
Yu-Lun Liu,
Chia-Ping Chen,
Yu-Lin Chang,
Hwann-Tzong Chen
Proceedings of European Conference on Computer Vision (ECCV), 2020  
project page
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arXiv
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code
We propose a data-driven approach, where a generative noise model is learned from real-world noise.
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Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
Yu-Lun Liu*,
Wei-Sheng Lai*,
Yu-Sheng Chen, Yi-Lung Kao,
Min-Hsuan Yang,
Yung-Yu Chuang,
Jia-Bin Huang
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020  
project page
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arXiv
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code
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demo
In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model.
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Learning to See Through Obstructions
Yu-Lun Liu,
Wei-Sheng Lai,
Min-Hsuan Yang,
Yung-Yu Chuang,
Jia-Bin Huang
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020  
project page
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arXiv
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code
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demo
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video
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New Scientists
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
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Attention-based View Selection Networks for Light-field Disparity Estimation
Yu-Ju Tsai,
Yu-Lun Liu,
Yung-Yu Chuang,
Ming Ouhyoung
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2020  
paper
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code
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benchmark
For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation.
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Deep Video Frame Interpolation using Cyclic Frame Generation
Yu-Lun Liu,
Yi-Tung Liao,
Yen-Yu Lin,
Yung-Yu Chuang
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2019   (Oral Presentation)
project page
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paper
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code
The cycle consistency loss can better utilize the training data to not only enhance the interpolation results, but also maintain the performance better with less training data.
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Background modeling using depth information
Yu-Lun Liu,
Hsueh-Ming Hang
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014  
paper
This paper mainly focuses on creating a global background model of a video sequence using the depth maps together with the RGB pictures.
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Virtual view synthesis using backward depth warping algorithm
Du-Hsiu Li,
Hsueh-Ming Hang,
Yu-Lun Liu
Picture Coding Symposium (PCS), 2013  
paper
In this study, we propose a backward warping process to replace the forward warping process, and the artifacts (particularly the ones produced by quantization) are significantly reduced.
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Stolen from Jon Barron's website.
Last updated December 2020.
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