Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain
| Autor: | Shunsuke Ono, Masaki Onuki, Masao Yamagishi, Yuichi Tanaka |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Mean squared error
Computer Networks and Communications business.industry Noise reduction 020206 networking & telecommunications Pattern recognition Image processing 02 engineering and technology Regularization (mathematics) Tikhonov regularization Frequency domain Signal Processing 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business Smoothing Information Systems Mathematics |
| Zdroj: | IEEE Transactions on Signal and Information Processing over Networks. 2:137-148 |
| ISSN: | 2373-7778 |
| DOI: | 10.1109/tsipn.2016.2532464 |
| Popis: | This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the graph spectral domain representation of the data-dependent filter, i.e., a spectral graph TF (SGTF). This representation enables us to design an effective graph signal denoising filter with a Tikhonov regularization. Moreover, for the proposed graph denoising filter, we provide a parameter optimization technique to search for a regularization parameter that approximately minimizes the mean squared error w.r.t. the unknown graph signal of interest. Comprehensive experimental results validate our graph signal processing-based approach for images and graph signals. |
| Databáze: | OpenAIRE |
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