1. Simultaneous Deep Stereo Matching and Dehazing with Feature Attention.
- Author
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Song, Taeyong, Kim, Youngjung, Oh, Changjae, Jang, Hyunsung, Ha, Namkoo, and Sohn, Kwanghoon
- Subjects
BINOCULAR vision ,ARTIFICIAL neural networks ,STEREO image ,MASS media - Abstract
Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and clear image from a hazy stereo image pair. Both tasks are synergistically formulated by fusing depth information from the matching cost and haze transmission. To learn the optimal fusion of depth-related features, we present a novel encoder-decoder architecture that extends the core idea of attention mechanism to the simultaneous stereo matching and dehazing. As a result, our method estimates high-quality disparity for the stereo images in scattering media, and produces appearance images with enhanced visibility. Finally, we further propose an effective strategy for adaptation to camera-captured images by distilling the cross-domain knowledge. Experiments on both synthetic and real-world scenarios including comparisons with state-of-the-art methods demonstrate the effectiveness and flexibility of our approach. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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