Image Denoising Using Superpixel-Based PCA
| Autor: | Sree Ramya S. P. Malladi, Jeffrey J. Rodriguez, Sundaresh Ram |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Noise measurement
Computer science business.industry Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image processing Least squares Computer Science Applications Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Signal Processing Principal component analysis Media Technology Noise (video) Artificial intelligence Electrical and Electronic Engineering business Decorrelation |
| Zdroj: | IEEE Transactions on Multimedia. 23:2297-2309 |
| ISSN: | 1941-0077 1520-9210 |
| DOI: | 10.1109/tmm.2020.3009502 |
| Popis: | Denoising is a fundamental task in image processing, aimed at estimating an unknown image from its noisy observation. In this paper, we develop a computationally simple paradigm for image denoising using superpixel grouping and principal component analysis (PCA) of similar patches within the superpixels. Our method comprises three steps. First, we perform a superpixel segmentation on the noisy images. Next, similar patches within the superpixels are grouped in order to preserve the local image structures. Finally, each group of similar patches is factorized by PCA transform and estimated by performing coefficient shrinkage in the PCA domain to remove the noise. The proposed method exploits the optimal energy compaction property of PCA on groups of similar patches in the least squares sense. The performance of our approach is experimentally verified on a variety of synthetic images at various noise levels, and on real world noisy images. Our proposed method achieves very competitive denoising performance, especially in preserving the fine image structures, compared with many existing denoising algorithms with respect to both objective measurement and visual evaluation. We also show that our proposed method is computationally more efficient than other local PCA based methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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