基于深度可分离膨胀卷积网络的轻量级高光谱图像超分辨率研究
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
AI总结：
针对高光谱图像超分辨率中模型参数多、训练样本少的问题，提出轻量级深度可分离膨胀卷积网络DSDCN，结合定制损失函数，在两个公开数据集上取得极具竞争力的性能。
摘要：
深度神经网络通过学习从低分辨率（LR）到高分辨率（HR）图像的映射，在自然图像超分辨率（SR）方面展现出极具竞争力的性能。然而，由于数据的高光谱维度和可用训练样本的稀缺性，高光谱超分辨率仍然是一个不适定问题。此外，现有方法通常依赖具有大量参数的大型模型，或者需要与全色或RGB图像进行融合，这两者在现实场景中往往不切实际。受MobileNet架构启发，我们引入了轻量级深度可分离膨胀卷积网络（DSDCN）来解决上述挑战。具体而言，我们的模型利用了多个类似于MobileNet架构的深度可分离卷积，并进一步引入膨胀卷积融合块，使模型在提取空间和光谱特征时更加灵活。此外，我们提出了一种结合均方误差（MSE）、基于L2范数正则化的约束以及基于光谱角损失的定制损失函数，确保了光谱和空间细节的保留。所提出的模型在两个公开的高光谱数据集上取得了极具竞争力的性能，使其非常适合高光谱图像超分辨率任务。源代码公开于：https://github.com/Usman1021/lightweight。
英文摘要：
Deep neural networks have demonstrated highly competitive performance in super-resolution (SR) for natural images by learning mappings from low-resolution (LR) to high-resolution (HR) images. However, hyperspectral super-resolution remains an ill-posed problem due to the high spectral dimensionality of the data and the scarcity of available training samples. Moreover, existing methods often rely on large models with a high number of parameters or require the fusion with panchromatic or RGB images, both of which are often impractical in real-world scenarios. Inspired by the MobileNet architecture, we introduce a lightweight depthwise separable dilated convolutional network (DSDCN) to address the aforementioned challenges. Specifically, our model leverages multiple depthwise separable convolutions, similar to the MobileNet architecture, and further incorporates a dilated convolution fusion block to make the model more flexible for the extraction of both spatial and spectral features. In addition, we propose a custom loss function that combines mean squared error (MSE), an L2 norm regularization-based constraint, and a spectral angle-based loss, ensuring the preservation of both spectral and spatial details. The proposed model achieves very competitive performance on two publicly available hyperspectral datasets, making it well-suited for hyperspectral image super-resolution tasks. The source codes are publicly available at: \href{https://github.com/Usman1021/lightweight}{https://github.com/Usman1021/lightweight}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
