Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

• URL: http://arxiv.org/abs/2108.13584v1
• Date: Tue, 31 Aug 2021 02:13:00 GMT
• Title: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
• Authors: Junjun Jiang and Chenyang Wang and Kui Jiang and Xianming Liu and Jiayi Ma
• Abstract summary: High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution. We present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples.
• Score: 82.59267937569213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofing attacks. However, the dense spectral bands of hyperspectral face images come at the cost of limited amount of photons reached a narrow spectral window on average, which greatly reduces the spatial resolution of hyperspectral face images. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution (HFSR), especially when the training samples are very limited. Benefiting from the amount of spectral bands, in which each band can be seen as an image, we present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples. In the shallow layers, we split the hyperspectral image into different spectral groups and take each of them as an individual training sample (in the sense that each group will be fed into the same network). Then, we gradually aggregate the neighbor bands at the deeper layers to exploit the spectral correlations. By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples to support the efficient training of the network and effectively exploit the spectral correlations among spectrum. To cope with the challenge of small training sample size (S3) problem, we propose to expand the training samples by a self-representation model and symmetry-induced augmentation. Experiments show that the introduced SSANet can well model the joint correlations of spatial and spectral information. By expanding the training samples, our proposed method can effectively alleviate the S3 problem. The comparison results demonstrate that our proposed method can outperform the state-of-the-arts.

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