Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images
Year: 2024
Type: article
Abstract: This research introduces the Adaptive Deep Residual Network (AdResNet), a deep convolutional neural network designed for effective image denoising in computer vision applications. Configured with the Adaptive White Shark Optimizer (AWSO), AdResNet removes noise while preserving key visual features. The model is tested on multiple noise types (Gaussian, Salt-and-Pepper, Poisson, and mixed noise) at various intensity levels, demonstrating versatility. Evaluations across medical, natural, and satellite images ensure its robustness for real-world applications. AdResNet achieves superior denoising results, with low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and high Structural Similarity Index Measure (SSIM). For example, the model recorded average metrics of MSE 13.61, PSNR 48.81 dB , and SSIM 0.96 on medical images, highlighting its efficacy. These results confirm AdResNet’s suitability for applications requiring high image quality, such as medical and satellite imaging.
Source: Ain Shams Engineering Journal
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $860
OpenAlex ID: W4404762735