Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data. We present a novel application of the algorithm as we use it to recover both noiseless and noisy images from overlapping patches. We implement a node network in Kubernetes using Docker containers to facilitate Cloud K-SVD. Results show that Cloud K-SVD can recover images approximately and remove quantifiable amounts of noise from benchmark gray-scaled images without sacrificing accuracy in recovery; we achieve an SSIM index of 0.88, 0.91 and 0.95 between clean and recovered images for noise levels ($\mu$ = 0, $\sigma^{2}$ = 0.01, 0.005, 0.001), respectively, which is similar to SOTA in the field. Cloud K-SVD is evidently able to learn a mutual dictionary across multiple nodes and remove AWGN from images. The mutual dictionary can be used to recover a specific image at any of the nodes in the network.

翻译：云K-SVD是一种字典学习算法，可在多个节点上训练，从而生成一个能够表征图像数据中低维几何结构的共享字典。我们提出该算法的新颖应用：利用其从重叠图像块中恢复无噪声和含噪图像。我们使用Docker容器在Kubernetes中构建节点网络以支持云K-SVD。结果表明，云K-SVD能够在不牺牲恢复精度的前提下，近似恢复图像并从标准灰度基准图像中去除可量化的噪声；对于噪声水平（$\mu$ = 0, $\sigma^{2}$ = 0.01, 0.005, 0.001），我们在干净图像与恢复图像之间分别实现了0.88、0.91和0.95的SSIM指数，与该领域当前最优方法性能相近。云K-SVD显然能够跨多个节点学习共享字典并去除图像中的加性高斯白噪声。该共享字典可用于网络中任意节点上特定图像的恢复。