报告题目：Single Image Dehazing via Multi-scale Convolutional Neural Networks
报告摘要：The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.
Single Image Dehazing via Multi-scale Convolutional Neural Networks, Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao1, and Ming-Hsuan Yang, ECCV, 2016
报告人简介：Wenqi Ren is currently a joint-training Ph.D. student with the School of Computer Science and Technology at Tianjin University, China, and Electrical Engineering and Computer Science at University of California, Merced, CA, USA. His research interest includes image deblurring, image/video analysis and enhancement, and related vision problems, the related research results have been published in authoritative journals and conferences, such as IEEE TIP, CVPR, ECCV, etc.
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