# VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising

arXiv: https://arxiv.org/abs/1710.00947 · HTML: https://mlsanity.com/paper/1710.00947/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 10 of 10)

- [Real-time Streaming Video Denoising with Bidirectional Buffers](https://mlsanity.com/paper/2207.06937/) (2022)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers](https://mlsanity.com/paper/2409.11256/) (2024)
- [Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond](https://mlsanity.com/paper/2103.14528/) (2021)
- [Image Denoising Using Green Channel Prior](https://mlsanity.com/paper/2408.05923/) (2024)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [Color Image Denoising Using The Green Channel Prior](https://mlsanity.com/paper/2402.08235/) (2024)
- [Pivotal Auto-Encoder via Self-Normalizing ReLU](https://mlsanity.com/paper/2406.16052/) (2024)
- [Efficient Image Denoising Using Global and Local Circulant Representation](https://mlsanity.com/paper/2508.10307/) (2025)
- [Image Denoising Using Global and Local Circulant Representation](https://mlsanity.com/paper/2512.23569/) (2025)

## Abstract (arXiv)

Techniques exploiting the sparsity of images in a transform domain have been effective for various applications in image and video processing. Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from streaming signals, which enjoy good convergence guarantees, and involve lower computational costs than online synthesis dictionary learning. In this work, we apply online transform learning to video denoising. We present a novel framework for online video denoising based on high-dimensional sparsifying transform learning for spatio-temporal patches. The patches are constructed either from corresponding 2D patches in successive frames or using an online block matching technique. The proposed online video denoising requires little memory, and offers efficient processing. Numerical experiments compare the performance to the proposed video denoising scheme but fixing the transform to be 3D DCT, as well as prior schemes such as dictionary learning-based schemes, and the state-of-the-art VBM3D and VBM4D on several video data sets, demonstrating the promising performance of the proposed methods.