# Robust Foreground-Background Separation for Severely-Degraded Videos Using Convolutional Sparse Representation Modeling

arXiv: https://arxiv.org/abs/2506.17838 · HTML: https://mlsanity.com/paper/2506.17838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [A survey of sparse representation: algorithms and applications](https://mlsanity.com/paper/1602.07017/) (2016)
- [Convolutional Dictionary Learning: A Comparative Review and New Algorithms](https://mlsanity.com/paper/1709.02893/) (2017)
- [Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements](https://mlsanity.com/paper/1503.01868/) (2015)
- [Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset](https://mlsanity.com/paper/1511.01245/) (2015)
- [Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video](https://mlsanity.com/paper/1712.06229/) (2017)
- [Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee](https://mlsanity.com/paper/2203.16328/) (2022)
- [Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models](https://mlsanity.com/paper/2212.14306/) (2022)
- [Efficient Constrained Signal Reconstruction by Randomized Epigraphical Projection](https://mlsanity.com/paper/1810.12249/) (2018)
- [Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping](https://mlsanity.com/paper/2404.03313/) (2024)
- [A General Destriping Framework for Remote Sensing Images Using Flatness Constraint](https://mlsanity.com/paper/2104.02845/) (2021)
- [Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications](https://mlsanity.com/paper/2301.08468/) (2023)
- [Joint Background-Anomaly-Noise Decomposition for Robust Hyperspectral Anomaly Detection via Constrained Convex Optimization](https://mlsanity.com/paper/2401.14814/) (2024)
- [Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization](https://mlsanity.com/paper/2302.08247/) (2023)

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

- [ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection](https://mlsanity.com/paper/2608.23923/) (2026)

## Abstract (arXiv)

This paper proposes a foreground-background separation (FBS) method with a novel foreground model based on convolutional sparse representation (CSR). In order to analyze the dynamic and static components of videos acquired under undesirable conditions, such as hardware, environmental, and power limitations, it is essential to establish an FBS method that can handle videos with low frame rates and various types of noise. Existing FBS methods have two limitations that prevent us from accurately separating foreground and background components from such degraded videos. First, they only capture either data-specific or general features of the components. Second, they do not include explicit models for various types of noise to remove them in the FBS process. To this end, we propose a robust FBS method with a CSR-based foreground model. This model can adaptively capture specific spatial structures scattered in imaging data. Then, we formulate FBS as a constrained multiconvex optimization problem that incorporates CSR, functions that capture general features, and explicit noise characterization functions for multiple types of noise. Thanks to these functions, our method captures both data-specific and general features to accurately separate the components from various types of noise even under low frame rates. To obtain a solution of the optimization problem, we develop an algorithm that alternately solves its two convex subproblems by newly established algorithms. Experiments demonstrate the superiority of our method over existing methods using two types of degraded videos: infrared and microscope videos.