Multi-Metric Near-Optimal Image Denoising
It is necessary to optimize the parameters for each image input to achieve the maximum denoising performance because the performance of denoising algorithms depends largely on the selection of the associated parameters. The commonly used objective image quality measures in quantitatively evaluating...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2022
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/83529 |
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| Sumario: | It is necessary to optimize the parameters for each image input to achieve the maximum denoising performance because the performance of denoising algorithms depends largely on the selection of the associated parameters. The commonly used objective image quality measures in quantitatively evaluating a denoised image are PSNR, SSIM, and MS-SSIM, which assume that the original image exists and is fully available as a reference. However, we do not have access to such reference images in many practical applications. Most existing methods for no-reference denoising parameter optimization either use the estimated noise distribution or a unique no-reference image quality evaluation measure. In the chapter, for BM3D, which is a state-of-the-art denoising algorithm, we introduce a natural image statistics (NIS) based on the generalized Gaussian distribution (GGD) and the elastic net regularization (EN) regression method and propose its use to perform the BM3D parameter optimization for PSNR, SSIM, and MS-SSIM, respectively, which are the popular image quality evaluation measures, without reference image and knowledge of the noise distribution. Experimental results with several images demonstrate the effectiveness of the proposed approach. |
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| ISBN: | 978-1-83768-029-0 |
| DOI: | 10.5772/intechopen.106710 |