论文标题
深度平衡与稀疏表示模型之间的连接，并应用于高光谱图像
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
论文作者
论文摘要
在这项研究中，考虑了计算多维视觉数据的稀疏表示的问题。通常，这样的数据，例如，高光谱图像，颜色图像或视频数据由表现出强大局部依赖性的信号组成。通过采用适合感兴趣信号属性的正则化项来得出一个新的计算高效稀疏编码优化问题。利用可学习的正则化技术的优点，使用神经网络充当结构，并揭示了基本的信号依赖性。为了解决优化问题，开发了深层展开和深层基于平衡的算法，形成了高度易于解释和简洁的基于深度学习的体系结构，以逐块的方式处理输入数据集。在高光谱图像降解的背景下，广泛的仿真结果得到了表明，所提出的算法的表现明显优于其他稀疏编码方法，并且与最新的基于深度学习的DeNoising模型相比，表现出卓越的性能。从更广泛的角度来看，我们的工作在经典方法（即稀疏表示理论）和基于深度学习建模的现代表示工具之间提供了独特的桥梁。
In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered. In general, such data e.g., hyperspectral images, color images or video data consists of signals that exhibit strong local dependencies. A new computationally efficient sparse coding optimization problem is derived by employing regularization terms that are adapted to the properties of the signals of interest. Exploiting the merits of the learnable regularization techniques, a neural network is employed to act as structure prior and reveal the underlying signal dependencies. To solve the optimization problem Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures, that process the input dataset in a block-by-block fashion. Extensive simulation results, in the context of hyperspectral image denoising, are provided, which demonstrate that the proposed algorithms outperform significantly other sparse coding approaches and exhibit superior performance against recent state-of-the-art deep-learning-based denoising models. In a wider perspective, our work provides a unique bridge between a classic approach, that is the sparse representation theory, and modern representation tools that are based on deep learning modeling.
