Inter and Intra Prior Learning-based Hyperspectral Image Reconstruction Using Snapshot SWIR Metasurface (2407.07503v3)

Abstract: Shortwave-infrared(SWIR) spectral information, ranging from 1 {\mu}m to 2.5{\mu}m, overcomes the limitations of traditional color cameras in acquiring scene information. However, conventional SWIR hyperspectral imaging systems face challenges due to their bulky setups and low acquisition speeds. This work introduces a snapshot SWIR hyperspectral imaging system based on a metasurface filter and a corresponding filter selection method to achieve the lowest correlation coefficient among these filters. This system offers the advantages of compact size and snapshot imaging. We propose a novel inter and intra prior learning unfolding framework to achieve high-quality SWIR hyperspectral image reconstruction, which bridges the gap between prior learning and cross-stage information interaction. Additionally, We design an adaptive feature transfer mechanism to adaptively transfer the contextual correlation of multi-scale encoder features to prevent detailed information loss in the decoder. Experiment results demonstrate that our method can reconstruct hyperspectral images with high speed and superior performance over existing methods.

• The paper develops a novel snapshot SWIR imaging system combining metasurface filters with an ERRA deep unfolding network for improved hyperspectral reconstruction.
• It introduces a farthest point sampling-based filter selection method that minimizes mutual correlation to boost spectral coding efficiency.
• Empirical evaluations demonstrate state-of-the-art PSNR and SSIM, underscoring the framework's potential for real-time hyperspectral imaging applications.

Analysis of Inter and Intra Prior Learning for Snapshot SWIR Hyperspectral Image Reconstruction

Hyperspectral imaging (HSI) is instrumental in various scientific and engineering domains, providing a wealth of spectral information across multiple applications. The paper, "Inter and Intra Prior Learning-based Hyperspectral Image Reconstruction Using Snapshot SWIR Metasurface," introduces a novel approach for enhancing the speed and quality of hyperspectral imaging, specifically within the Shortwave Infrared (SWIR) spectrum. This work proposes an advanced snapshot imaging system leveraging metasurface filters, along with an innovative deep unfolding network termed ERRA, to optimize hyperspectral image reconstruction.

Technical Contributions

The primary contributions of this research are the development of a compact and efficient snapshot imaging system and the introduction of a sophisticated image reconstruction framework:

1. Snapshot Imaging System: The research presents a SWIR imaging system that utilizes metasurface filters to overcome the limitations of traditional hyperspectral systems. By adopting a snapshot approach, the system is both more compact and faster, facilitating its deployment in dynamic and real-time applications.
2. Filter Selection Technique: A significant advancement is the method for selecting metasurface filters. By minimizing mutual correlation within the array, the system enhances spectral coding efficiency. This is achieved through a farthest point sampling-based optimization, which ensures lower correlation between selected filters, thereby supporting accurate spectral reconstruction.
3. ERRA Network Design: The deep unfolding network, or ERRA, integrates both inter and intra-stage prior learning for spectral reconstruction. The network employs a spatial-spectral prior learning block alongside a low-rank prior learning mechanism to leverage the HSI's intrinsic properties effectively.
4. Adaptive Feature Transfer: An integral part of the ERRA network is its adaptive feature transfer block. This mechanism efficiently transfers contextual information between encoder and decoder stages, thus preserving detailed features and reducing information loss—a prevalent issue in existing models.

Empirical Evaluation

The performance evaluation using the AVIRIS-NG dataset demonstrates that the proposed method achieves state-of-the-art results in terms of PSNR and SSIM when compared to existing methods such as TSANet, ADMM-Net, HDNet, and RDLUF. The paper shows consistent performance improvements, indicating the effectiveness of the proposed filter selection and reconstruction methods.

Implications and Future Prospects

The implications of this work are multifaceted:

• Practical Applications: The advancement of compact, fast, and accurate SWIR imaging systems could benefit fields ranging from remote sensing to medical diagnostics, where the speed and portability of imaging equipment are crucial.
• Theoretical Insights: By integrating inter-stage and intra-stage information learning, the paper sets a precedent for future HSI research to explore more complex joint optimization scenarios, which could yield deeper insights into the spectral properties of scenes.
• Technological Advancements: The paper opens up new opportunities for developing intelligent imaging systems that utilize deep learning frameworks aligned with hardware-specific optimizations, potentially influencing future AI developments in imaging technology.

This work aligns with the trend of integrating optical engineering with artificial intelligence, providing a robust framework that could be extended or adapted to other spectral ranges or imaging applications. This paper is a noteworthy contribution to the field, paving the way for more integrated and efficient hyperspectral imaging solutions.