CMSNet: Deep Color and Monochrome Stereo
In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras, called CMSNet (Color and Monochrome Stereo Network). Both cameras have the same structure except for the presence of a Bayer filter, but have a fundamental trade-off. The Bayer...
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Published in | International journal of computer vision Vol. 130; no. 3; pp. 652 - 668 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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01.03.2022
Springer Springer Nature B.V |
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Abstract | In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras,
called
CMSNet (Color and Monochrome Stereo Network). Both cameras have the same structure except for the presence of a Bayer filter, but have a fundamental trade-off. The Bayer filter allows capturing chrominance information of scenes, but limits a quantum efficiency of cameras, which causes severe image noise. It seems ideal if we can take advantage of both the cameras so that we obtain noise-free images with their corresponding disparity maps. However, image luminance recorded from a color camera is not consistent with that from a monochrome camera due to spatially-varying illumination and different spectral sensitivities of the cameras. This degrades the performance of stereo matching. To solve this problem, we design CMSNet for disparity estimation from noisy color and relatively clean monochrome images. CMSNet also infers a noise-free image with the estimated disparity map. We leverage a data augmentation to simulate realistic signal-dependent noise and various radiometric distortions between input stereo pairs to train CMSNet effectively. CMSNet is evaluated using various datasets and the performance of our disparity estimation and image enhancement consistently outperforms state-of-the-art methods. |
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AbstractList | In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras, called CMSNet (Color and Monochrome Stereo Network). Both cameras have the same structure except for the presence of a Bayer filter, but have a fundamental trade-off. The Bayer filter allows capturing chrominance information of scenes, but limits a quantum efficiency of cameras, which causes severe image noise. It seems ideal if we can take advantage of both the cameras so that we obtain noise-free images with their corresponding disparity maps. However, image luminance recorded from a color camera is not consistent with that from a monochrome camera due to spatially-varying illumination and different spectral sensitivities of the cameras. This degrades the performance of stereo matching. To solve this problem, we design CMSNet for disparity estimation from noisy color and relatively clean monochrome images. CMSNet also infers a noise-free image with the estimated disparity map. We leverage a data augmentation to simulate realistic signal-dependent noise and various radiometric distortions between input stereo pairs to train CMSNet effectively. CMSNet is evaluated using various datasets and the performance of our disparity estimation and image enhancement consistently outperforms state-of-the-art methods. In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras, called CMSNet (Color and Monochrome Stereo Network). Both cameras have the same structure except for the presence of a Bayer filter, but have a fundamental trade-off. The Bayer filter allows capturing chrominance information of scenes, but limits a quantum efficiency of cameras, which causes severe image noise. It seems ideal if we can take advantage of both the cameras so that we obtain noise-free images with their corresponding disparity maps. However, image luminance recorded from a color camera is not consistent with that from a monochrome camera due to spatially-varying illumination and different spectral sensitivities of the cameras. This degrades the performance of stereo matching. To solve this problem, we design CMSNet for disparity estimation from noisy color and relatively clean monochrome images. CMSNet also infers a noise-free image with the estimated disparity map. We leverage a data augmentation to simulate realistic signal-dependent noise and various radiometric distortions between input stereo pairs to train CMSNet effectively. CMSNet is evaluated using various datasets and the performance of our disparity estimation and image enhancement consistently outperforms state-of-the-art methods. |
Audience | Academic |
Author | Choe, Jaesung Lee, Joon-Young Im, Sunghoon Kang, Minjun Jeon, Hae-Gon Hebert, Martial |
Author_xml | – sequence: 1 givenname: Hae-Gon surname: Jeon fullname: Jeon, Hae-Gon organization: AI Graduate School & The School of Electrical Engineering and Computer Science, GIST – sequence: 2 givenname: Sunghoon orcidid: 0000-0001-9776-8101 surname: Im fullname: Im, Sunghoon email: sunghoonim@dgist.ac.kr organization: Department of Electrical Engineering and Computer Science, DGIST – sequence: 3 givenname: Jaesung surname: Choe fullname: Choe, Jaesung organization: KAIST – sequence: 4 givenname: Minjun surname: Kang fullname: Kang, Minjun organization: KAIST – sequence: 5 givenname: Joon-Young surname: Lee fullname: Lee, Joon-Young organization: Adobe Research – sequence: 6 givenname: Martial surname: Hebert fullname: Hebert, Martial organization: The Robotics Institute, Carnegie Mellon University |
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Keywords | Disparity estimation Stereo matching Convolutional neural network Image enhancement |
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Snippet | In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras,
called
CMSNet (Color and Monochrome... In this paper, we propose an end-to-end convolutional neural network for stereo matching with color and monochrome cameras, called CMSNet (Color and Monochrome... |
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SubjectTerms | Artificial Intelligence Artificial neural networks Cameras Color matching Computer Imaging Computer Science Image enhancement Image Processing and Computer Vision Luminance Neural networks Noise Pattern Recognition Pattern Recognition and Graphics Performance degradation Quantum efficiency Special Issue on 3D Computer Vision Spectral sensitivity Vision |
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Title | CMSNet: Deep Color and Monochrome Stereo |
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