Clustering-Based 3-D-MAP Despeckling of SAR Images Using Sparse Wavelet Representation
| Autor: | Clara Cruz-Ramos, Rogelio Reyes-Reyes, Gibran Aranda-Bojorges, Volodymyr Ponomaryov, Sergiy Sadovnychiy |
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| Rok vydání: | 2022 |
| Předmět: |
Discrete wavelet transform
Computer science business.industry Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Sparse approximation Geotechnical Engineering and Engineering Geology Speckle pattern Wavelet Dimension (vector space) Computer Science::Computer Vision and Pattern Recognition Maximum a posteriori estimation Artificial intelligence Electrical and Electronic Engineering business Cluster analysis |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 19:1-5 |
| ISSN: | 1558-0571 1545-598X |
| Popis: | Image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum a posteriori (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception. |
| Databáze: | OpenAIRE |
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