# Cloud K-SVD for Image Denoising

arXiv: https://arxiv.org/abs/2303.00755 · HTML: https://mlsanity.com/paper/2303.00755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data](https://mlsanity.com/paper/1412.7839/) (2014)
- [Big Data: Understanding Big Data](https://mlsanity.com/paper/1601.04602/) (2016)
- [Distributed Private Online Learning for Social Big Data Computing over Data Center Networks](https://mlsanity.com/paper/1602.06489/) (2016)
- [Pruned non-local means](https://mlsanity.com/paper/1701.08280/) (2017)

## Built on by (most cited first, 1 of 1)

- [DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces](https://mlsanity.com/paper/2505.18441/) (2025)

## Abstract (arXiv)

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 (μ = 0, σ2 = 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.