Abstract
Recent approaches based on transform-based tensor nuclear norm (TNN) have demonstrated notable effectiveness in hyperspectral image (HSI) inpainting by leveraging low-rank structures in latent representations. Recent developments incorporate deep transforms to improve low-rank tensor representation; however, existing approaches typically restrict the transform to the spectral mode, neglecting low-rank properties along other tensor modes. In this paper, we propose a novel 3-directional deep low-rank tensor representation (3DeepRep) model, which performs deep nonlinear transforms along all three modes of the HSI tensor. To enforce low-rankness, the model minimizes the nuclear norms of mode-i frontal slices in the corresponding latent space for each direction (i=1,2,3), forming a 3-directional TNN regularization. The outputs from the three directional branches are subsequently fused via a learnable aggregation module to produce the final result. An efficient gradient-based optimization algorithm is developed to solve the model in a self-supervised manner. Extensive experiments on real-world HSI datasets demonstrate that the proposed method achieves superior inpainting performance compared to existing state-of-the-art techniques, both qualitatively and quantitatively.
Abstract (translated)
最近基于变换的张量核范数（TNN）的方法在高光谱图像（HSI）修复中展示了显著的有效性，通过利用潜在表示中的低秩结构。近期的发展将深度转换纳入其中以改进低秩张量表示；然而，现有方法通常只限制变换在光谱模式上进行，忽略了其他张量模式上的低秩特性。本文提出了一个新颖的三方向深度低秩张量表示（3DeepRep）模型，在HSI张量的所有三个模式上执行深度非线性转换。为了强制执行低秩性质，该模型通过最小化每个方向对应的潜在空间中mode-i前切片的核范数来形成三向TNN正则化（i=1,2,3）。来自三个方向分支的输出随后通过一个可学习的聚合模块融合以生成最终结果。开发了一种有效的基于梯度的优化算法，以便自监督地解决该模型。在真实世界HSI数据集上的大量实验表明，所提出的方法在定性和定量上均优于现有的最先进的技术，在图像修复性能方面表现出色。
URL
https://arxiv.org/abs/2506.16735
