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Semantic segmentation methods are typically designed for RGB color images, which are interpolated from raw Bayer images. While RGB images provide abundant color information and are easily understood by humans, they also add extra storage and computational burden for neural networks. On the other hand, raw Bayer images preserve primitive color information with a single channel, potentially increasing segmentation accuracy while significantly decreasing storage and computation time. In this paper, we propose RawSeg-Net to segment single-channel raw Bayer images directly. Different from RGB images that already contain neighboring context information during ISP color interpolation, each pixel in raw Bayer images does not contain any context clues. Based on Bayer pattern properties, RawSeg-Net assigns dynamic attention on Bayer images' spectral frequency and spatial locations to mitigate classification confusion, and proposes a re-sampling strategy to capture both global and local contextual information.
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Efficient Diffeomorphic Image Registration using Multi-Scale Dual-Phased Learning
Diffeomorphic registration faces challenges for high dimensional images, especially in terms of memory limits. Existing approaches either downsample/crop original images or approximate underlying transformations to reduce the model size. To mitigate this, we propose a Dividing and Down-sampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales while reducing memory cost. DDR-Net leverages the global context via downsampling the input and utilizes local details by dividing the input images to subvolumes. Such design fuses global and local information and obtains both coarse- and fine-level alignments in the final deformation fields. We apply DDR-Net to the OASIS dataset. The proposed simple yet effective architecture is a general method and could be extended to other registration architectures for better performance with limited computing resources.
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- Award ID(s):
- 1755970
- PAR ID:
- 10350886
- Date Published:
- Journal Name:
- IEEE 19th International Symposium on Biomedical Imaging (ISBI)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Major semantic segmentation approaches are designed for RGB color images, which is interpolated from raw Bayer images. The use of RGB images on the one hand provides abundant scene color information. On the other hand, RGB images are easily observable for human users to understand the scene. The RGB color continuity also facilitates researchers to design segmentation algorithms, which becomes unnecessary in end-to-end learning. More importantly, the use of 3 channels adds extra storage and computation burden for neural networks. In contrast, the raw Bayer images can reserve the primitive color information in the largest extent with just a single channel. The compact design of Bayer pattern not only potentially increases a higher segmentation accuracy because of avoiding interpolation, but also significantly decreases the storage requirement and computation time in comparison with standard R, G, B images. In this paper, we propose BayerSeg-Net to segment single channel raw Bayer image directly. Different from RGB color images that already contain neighboring context information during ISP color interpolation, each pixel in raw Bayer images does not contain any context clues. Based on Bayer pattern properties, BayerSeg-Net assigns dynamic attention on Bayer images' spectral frequency and spatial locations to mitigate classification confusion, and proposes a re-sampling strategy to capture both global and local contextual information. We demonstrate the usability of raw Bayer images in segmentation tasks and the efficiency of BayerSeg-Net on multiple datasets.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISBI52829.2022.9761693
