HsRu: hyperspectral restoration U-Net

Meng Li; Zhanjiang Yang; Xiaoxin An; Ju Li; Guodong Han; Zhongwu Wang

doi:10.1117/12.3034150

13 June 2024 HsRu: hyperspectral restoration U-Net

Meng Li, Zhanjiang Yang, Xiaoxin An, Ju Li, Guodong Han, Zhongwu Wang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131802J (2024) https://doi.org/10.1117/12.3034150
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Recent advancements in spectral reconstruction algorithms have yielded impressive results for RGB image restoration in hyperspectral tasks, utilizing Convolutional Neural Networks and Transformer networks. However, there are certain limitations associated with these approaches. The design of CNN networks is gradually developing towards deeper networks, which can hinder their ability to capture long-distance dependencies and similarities. Transformer networks effectively address this issue but introduce information redundancy, complex operations, and slow reasoning speeds. To address these concerns and strike a balance between information efficiency, algorithm accuracy, and real-time inference on end device, we propose a Hyperspace Reconstruction Unet (HsRu) network. Remarkably, the HsRu network features a minimal parameter count of only 200KB, constituting just 8% of the parameters found in the leading model, which has 2.25M parameters, yet it manages to maintain the root mean square error at a low 0.03. Unlike traditional Unet architectures, HsRu employs only two downsampling and upsampling stages to preserve high resolution and limit channel-level modeling. The key innovation in HsRu lies in the cross spatial reconstruction unit, which efficiently separates and reorganizes feature map information, balancing effectiveness and redundancy. Additionally, the skip connection components incorporate the CSRU to cross-combine spatial details between corresponding upsampling and downsampling layers, facilitating the fusion of information from various depths. This approach minimizes redundancy while maintaining a high level of information richness, ensuring optimal spectral reconstruction results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meng Li, Zhanjiang Yang, Xiaoxin An, Ju Li, Guodong Han, and Zhongwu Wang "HsRu: hyperspectral restoration U-Net", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131802J (13 June 2024); https://doi.org/10.1117/12.3034150

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KEYWORDS

Image restoration

Hyperspectral imaging

Network architectures

Transformers

RGB color model

Reconstruction algorithms

Data modeling

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