# Graph Spectral Image Processing

arXiv: https://arxiv.org/abs/1801.04749 · HTML: https://mlsanity.com/paper/1801.04749/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 30 of 42)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Graph Filters for Signal Processing and Machine Learning on Graphs](https://mlsanity.com/paper/2211.08854/) (2022)
- [How Framelets Enhance Graph Neural Networks](https://mlsanity.com/paper/2102.06986/) (2021)
- [Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration](https://mlsanity.com/paper/2207.10447/) (2022)
- [Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors](https://mlsanity.com/paper/2406.04090/) (2024)
- [Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment](https://mlsanity.com/paper/2208.08726/) (2022)
- [Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention](https://mlsanity.com/paper/2304.00335/) (2023)
- [Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming](https://mlsanity.com/paper/2409.07794/) (2024)
- [Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks](https://mlsanity.com/paper/2202.13526/) (2022)
- [Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer](https://mlsanity.com/paper/2307.02625/) (2023)
- [Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction](https://mlsanity.com/paper/2311.13533/) (2023)
- [Optimizing k in kNN Graphs with Graph Learning Perspective](https://mlsanity.com/paper/2401.08245/) (2024)
- [Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment](https://mlsanity.com/paper/2106.01642/) (2021)
- [Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment](https://mlsanity.com/paper/2110.11420/) (2021)
- [Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks](https://mlsanity.com/paper/2112.01714/) (2021)
- [Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation](https://mlsanity.com/paper/2207.14019/) (2022)
- [Motion estimation and filtered prediction for dynamic point cloud attribute compression](https://mlsanity.com/paper/2210.08262/) (2022)
- [Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution](https://mlsanity.com/paper/2302.03933/) (2023)
- [Graph Sparsification for GCN Towards Optimal Crop Yield Predictions](https://mlsanity.com/paper/2306.01725/) (2023)
- [Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses](https://mlsanity.com/paper/2401.01813/) (2024)
- [Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention](https://mlsanity.com/paper/2506.23542/) (2025)
- [Sparse Graph Learning from Sparse Data via Fiedler Number Maximization](https://mlsanity.com/paper/2604.26132/) (2026)
- [Statistical Testing on Directed Graphs by Surrogate Data Generation](https://mlsanity.com/paper/2606.00758/) (2026)
- [Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding](https://mlsanity.com/paper/2112.07862/) (2021)
- [Hybrid Model-based / Data-driven Graph Transform for Image Coding](https://mlsanity.com/paper/2203.01186/) (2022)
- [Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising](https://mlsanity.com/paper/2207.11050/) (2022)
- [Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction](https://mlsanity.com/paper/2208.02714/) (2022)
- [Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity](https://mlsanity.com/paper/2211.13800/) (2022)
- [Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks](https://mlsanity.com/paper/2303.00944/) (2023)
- [Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment](https://mlsanity.com/paper/2408.01859/) (2024)

## Abstract (arXiv)

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation.