School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
Sensors (Basel). 2021 Mar 3;21(5):1743. doi: 10.3390/s21051743.
Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel.
基于深度学习的超分辨率 (SR) 算法在各种任务中占据主导地位，包括医学成像、街景监控和人脸识别。在遥感领域，当前大多数 SR 方法利用直接将高分辨率 (HR) 图像双三次下采样得到的低分辨率 (LR) 图像作为训练集和测试集，从而实现了高 PSNR/SSIM 分数，但在应用中表现出性能下降，因为遥感图像的退化模型受到未知参数的高斯模糊的影响。受多任务学习策略的启发，我们提出了一种多模糊核超分辨率框架 (MSF)，其中多模糊核学习模块 (MLM) 优化了网络的参数，使网络对于具有不同模糊核的 SR 过程具有可转移和敏感的特性。此外，为了同时利用大规模遥感图像的先验信息和单个测试图像中的递归信息，我们的框架中利用了类特征捕获模块 (CCM) 和无监督学习模块 (ULM)。广泛的实验表明，我们的框架在具有未知高斯模糊核的遥感图像 SR 方面优于当前最先进的 SR 算法。
