Cloud K-SVD for Image Denoising

To remove Additive White Gaussian Noise (AWGN) from images using Cloud K-SVD, a collaborative dictionary learning algorithm. 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 noiseless and recovered images for noise levels (μ = 0, σ2 = 0.01, 0.005, 0.001), respectively, which is on a par with 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. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.