연구성과
CMSNet: Deep Color and Monochrome Stereo
- 작성자관리자
- 작성일2023.09.14
- 조회수102
Hae-Gon Jeon, Sunghoon Im, Jaesung Choe, Minjun Kang, Joon-Young Lee, Martial Hebert
상세내용
BibTeX
- 저자 : Hae-Gon Jeon, Sunghoon Im, Jaesung Choe, Minjun Kang, Joon-Young Lee, Martial Hebert
- 논문명 : CMSNet: Deep Color and Monochrome Stereo
- 학술지명 : International Journal of Computer Vision
- 발간월 : January
- SCI-E 여부 : SCI-E
- ISSN : 0920-5691
- DOI : https://doi.org/10.1007/s11263-021-01565-6
- 초록 : 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.
첨부파일
- s11263-021-01565-6.pdf (2.7 MB)