Recent developments in computational color image denoising with PDEs to deep learning: a review
Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state of the art mathematical models for computational color image denoising. These algorithms are divided into methods that are based on the...
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| Veröffentlicht in: | The Artificial intelligence review 2021-12, Vol.54 (8), p.6245-6276 |
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| container_title | The Artificial intelligence review |
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| creator | Salamat, Nadeem Missen, Malik Muhammad Saad Surya Prasath, V. B. |
| description | Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state of the art mathematical models for computational color image denoising. These algorithms are divided into methods that are based on the partial differential equations, low rank, sparse representation and recent developments based on deep learning models. These algorithms also compared in terms of image quality measures. Our analysis and review of the computational color image denoising filters indicate that the convolutional neural networks from the deep learning domain obtain high quality restorations in terms of image quality despite the higher computational complexity. |
| doi_str_mv | 10.1007/s10462-021-09977-z |
| format | Article |
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| subjects | Algorithms Analysis Artificial Intelligence Artificial neural networks Color imagery Computer Science Deep learning Differential equations Image filters Image processing Image quality Machine learning Mathematical models Neural networks Noise reduction Partial differential equations Rankings |
| title | Recent developments in computational color image denoising with PDEs to deep learning: a review |
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