Improving image quality via Deep learning: Aspects of speckle noise reduction applying the optimal weighted filter
This article explores the dynamic field of image denoising, with a specific emphasis on the novel Adaptive Euclidean Weighted Filters (AEWF) and their utilization in mitigating speckle noise. The inadequacy of conventional filters to handle the intricacies of contemporary applications has prompted r...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | México |
| Recursos: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/2435 |
| Acesso em linha: | https://jart.icat.unam.mx/index.php/jart/article/view/2435 |
| Access Level: | acceso abierto |
| Palavra-chave: | Convolution neural networks Deep learning speckle noise adaptive weighted filter bat optimization algorithm |
| Resumo: | This article explores the dynamic field of image denoising, with a specific emphasis on the novel Adaptive Euclidean Weighted Filters (AEWF) and their utilization in mitigating speckle noise. The inadequacy of conventional filters to handle the intricacies of contemporary applications has prompted researchers to investigate the potential benefits of integrating deep learning and optimization algorithms. By combining Convolutional Neural Networks (CNNs) and the Bat Optimization Algorithm (BOA), we develop an adaptive dynamic AEWF that prioritizes the reduction of speckle noise while accommodating various image attributes. By emulating the echolocation behavior of bats, BOA optimizes denoising parameters to guarantee outputs of superior quality. Convolutional and ReLU layers of CNNs are crucial during the denoising phase, while pooling layers are utilized to reduce the size of the image. Our comprehensive investigation contrasts the suggested methodology with traditional filters, evaluating its efficacy in mitigating speckle noise. The benchmarking metrics, namely the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), have respective mean values of 32.05 and 0.98. Through an exhaustive evaluation of feature preservation, noise reduction, and overall image quality, this study underscores the integrated AEWF's effectiveness in tackling current obstacles in the field of image processing. |
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