Abstract
Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision. It involves ill-posed inverse problems, such as inpainting and super-resolution. Although deep learning methods have transformed the field through data-driven learning, their effectiveness hinges on access to meticulously curated ground-truth datasets. This fundamentally restricts their applicability in real-world scenarios where such data is unavailable. This paper presents SHARE (Single Hyperspectral Image Restoration with Equivariance), a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling to eliminate the need for ground truth. SHARE's core concept is to exploit the intrinsic invariance of hyperspectral structures under differentiable geometric transformations (e.g. rotations and scaling) to derive self-supervision signals through equivariance consistency constraints. Our novel Dynamic Adaptive Spectral Attention (DASA) module further enhances this paradigm shift by explicitly encoding the global low-rank property of HSI and adaptively refining local spectral-spatial correlations through learnable attention mechanisms. Extensive experiments on HSI inpainting and super-resolution tasks demonstrate the effectiveness of SHARE. Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods. We hope that our approach will shed new light on HSI restoration and broader scientific imaging scenarios. The code will be released at this https URL.
Abstract (translated)
高光谱图像（HSI）恢复是计算成像和计算机视觉领域的一个基本挑战。它涉及到如插值和超分辨率之类的病态逆问题。尽管深度学习方法通过数据驱动的学习极大地改变了该领域，但它们的有效性依赖于对精心策划的地面实况数据集的访问。这种根本性的限制阻碍了这些方法在现实世界场景中的应用，因为在这些场景中通常没有可用的数据。本文提出了SHARE（具有等变性的单高光谱图像恢复），这是一种完全无监督的框架，它将几何等变性原理与低秩光谱建模相结合，以消除对地面实况数据的需求。 SHARE的核心概念是利用在可微分几何变换（例如旋转和缩放）下的高光谱结构固有的不变性，通过等价一致性约束来推导自监督信号。我们的新颖的动态自适应光谱注意（DASA）模块进一步增强了这一范式转变，该模块明确地编码了HSI的整体低秩属性，并通过可学习的关注机制自适应地细化局部光谱-空间相关性。 在HSI插值和超分辨率任务上的大量实验展示了SHARE的有效性。我们的方法优于许多最先进的无监督方法，在性能上与有监督的方法相当。我们希望我们的方法将为高光谱图像恢复以及更广泛的科学成像场景带来新的见解。代码将在[此链接](https://example.com)发布。
URL
https://arxiv.org/abs/2601.13987
