高光谱图像超分辨率的空间-光谱细化学习与互补观测校准

中文摘要 AI 辅助

高光谱与多光谱图像融合（HMIF）旨在结合高分辨率多光谱图像（HR-MSI）的精细空间细节与低分辨率高光谱图像（LR-HSI）的丰富光谱信息，重建高分辨率高光谱图像（HR-HSI）。近期隐式神经表征（INRs）的进展为HMIF提供了灵活的基于坐标的建模能力，但现有基于INR的方法可能无法充分捕捉细粒度空间结构与丰富光谱依赖关系；此外，LR-HSI与HR-MSI主要通过退化一致性约束融合，其互补信息未得到充分利用。为解决这些局限，本文提出带隐式张量神经表征的两阶段重建方法（TSR-ITNR），这是一个集成表征细化与观测引导校准的统一自监督框架。第一阶段中，TSR-ITNR学习隐式Tucker表征，细化其低秩空间系数张量与光谱基，以更好捕捉精细空间结构与波段间相关性；固定预训练去噪器为初步重建提供深度先验。第二阶段中，无参数校准从两类观测中推导互补且互不干扰的校正，以恢复第一阶段未充分捕捉的信息。理论分析验证了光谱细化的保几何特性与校准的正交互补性。在多个基准数据集上的大量实验表明，TSR-ITNR在无HR-HSI真值监督的情况下，具备优异的定量、视觉及光谱重建性能；除传统重建指标外，本文还通过下游语义分割准确率评估了TSR-ITNR的有效性。

英文摘要

Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) by combining the fine spatial details of a high-resolution multispectral image (HR-MSI) with the rich spectral information of a low-resolution hyperspectral image (LR-HSI). Recent advances in implicit neural representations (INRs) have enabled flexible coordinate-based modeling for HMIF; however, existing INR-based approaches may not fully capture fine-grained spatial structures and rich spectral dependencies. Moreover, the LR-HSI and HR-MSI are primarily incorporated through degradation-consistency constraints, leaving their complementary information underexploited. To address these limitations, we propose Two-Stage Reconstruction with Implicit Tensor Neural Representation (TSR-ITNR), a unified self-supervised framework integrating representation refinement and observation-guided calibration. In Stage 1, TSR-ITNR learns an implicit Tucker representation and refines its low-rank spatial coefficient tensor and spectral basis to better capture fine spatial structures and interband correlations. A fixed pretrained denoiser further provides a deep prior for the preliminary reconstruction. In Stage 2, parameter-free calibration derives complementary and noninterfering corrections from both observations to recover information insufficiently captured in Stage 1. Theoretical analysis establishes the geometry-preserving property of spectral refinement and the orthogonal complementarity of calibration. Extensive experiments on multiple benchmark datasets demonstrate strong quantitative, visual, and spectral reconstruction performance without ground-truth HR-HSI supervision. Beyond conventional reconstruction metrics, we further assess the effectiveness of TSR-ITNR using downstream semantic segmentation accuracy.

发表机构

• School of Management, Zhengzhou University（郑州大学管理学院）

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