Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering

Huang, Qiwei; Li, Ruikang; Jiang, Zidong; Feng, Wei; Lin, Sijie; Feng, Hui; Hu, Bo

doi:10.1109/ieeeconf44664.2019.9048703

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…To boost smoothing, [232] develops the trilateral filter, which has a rational graph frequency response, explaining its improved performance. GCFs are also used for guided image filtering in [233], [234].…”

Section: Image Processingmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

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Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.

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Section: Image Processingmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this context, signal reconstruction techniques are developed to substitute the missing signals with dependable estimations. Furthermore, graph signal processing (GSP) [1][2][3] is introduced as an intuitive framework to deal with graph signals lying on an irregular structure, which applies to a wide class of classical use cases such as traffic [4], sensor network [5], 3D point cloud [6,7], image processing [8], air pollution monitoring platform [9], and recommendation system [10]. In the realm of GSP tools, the task of reconstruction emerges as a straightforward approach to address the intricate challenge of estimating missing signals.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm with Fast Convergence Rate for Sparse Graph Signal Reconstruction

Cao,

Jiang,

Wang

et al. 2023

Preprint

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In this paper, we consider the graph signals are sparse in the graph Fourier domain and propose an iterative threshold compressed sensing reconstruction (ITCSR) algorithm to reconstruct sparse graph signals in the graph Fourier domain. The proposed ITCSR algorithm derives from the well-known compressed sensing (CS) by considering a threshold for sparsity-promoting reconstruction of the under- lying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine an suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm (SSA). Morever, we analytically prove the convergence property of the proposed ITCSR algorithm. In the experimental, numerical tests with synthetic as well as 3D point cloud data demonstrate the merits of the proposed ITCSR algorithm relative to the baseline algorithms.

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Depth Image Denoising Using Graph-Based Sparse Representation

Zhang

Jiang

et al. 2021

2021 4th International Conference on Information Communication and Signal Processing (ICICSP)

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Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering

Cited by 7 publications

References 27 publications

Graph Filters for Signal Processing and Machine Learning on Graphs

Graph Filters for Signal Processing and Machine Learning on Graphs

An Efficient Algorithm with Fast Convergence Rate for Sparse Graph Signal Reconstruction

Depth Image Denoising Using Graph-Based Sparse Representation

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