A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction

• URL: http://arxiv.org/abs/2301.10208v1
• Date: Tue, 24 Jan 2023 18:28:21 GMT
• Title: A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction
• Authors: Junyu Wang, Shijie Wang, Wenyu Liu, Zengqiang Zheng, Xinggang Wang
• Abstract summary: We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design. SAUNet can be scaled to non-trivial 13 stages with continuous improvement. We set new records on CAVE and KAIST HSI reconstruction benchmarks.
• Score: 33.53825801739728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design with an adaptive alternate optimization framework for hyperspectral image (HSI) reconstruction. SAUNet customizes a Residual Adaptive ADMM Framework (R2ADMM) to connect each stage of the network via a group of learnable parameters to promote the usage of mask prior, which greatly stabilizes training and solves the accuracy degradation issue. Additionally, we introduce a simple convolutional modulation block (CMB), which leads to efficient training, easy scale-up, and less computation. Coupling these two designs, SAUNet can be scaled to non-trivial 13 stages with continuous improvement. Without bells and whistles, SAUNet improves both performance and speed compared with the previous state-of-the-art counterparts, which makes it feasible for practical high-resolution HSI reconstruction scenarios. We set new records on CAVE and KAIST HSI reconstruction benchmarks. Code and models are available at https://github.com/hustvl/SAUNet.

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