Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images
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....
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005690 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841526261033205760 |
|---|---|
| author | Mary Charles Sheeba Christopher Seldev Christopher |
| author_facet | Mary Charles Sheeba Christopher Seldev Christopher |
| author_sort | Mary Charles Sheeba |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-39bc11e454fc43b4a5bace19dddf4ba0 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-39bc11e454fc43b4a5bace19dddf4ba02025-01-17T04:49:20ZengElsevierAin Shams Engineering Journal2090-44792025-01-01161103188Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite imagesMary Charles Sheeba0Christopher Seldev Christopher1Research Scholar, Departement of Computer Science and Engineering, St. Xavier's Catholic College of Engineering, India; Corresponding author.Professor, Departement of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Chunkankadai, IndiaThis 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.http://www.sciencedirect.com/science/article/pii/S2090447924005690Image denoisingDeep convolutional neural networkDeep learningNoise reductionImage processingAdaptive white shark optimizer |
| spellingShingle | Mary Charles Sheeba Christopher Seldev Christopher Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images Ain Shams Engineering Journal Image denoising Deep convolutional neural network Deep learning Noise reduction Image processing Adaptive white shark optimizer |
| title | Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images |
| title_full | Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images |
| title_fullStr | Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images |
| title_full_unstemmed | Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images |
| title_short | Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images |
| title_sort | adaptive deep residual network for image denoising across multiple noise levels in medical nature and satellite images |
| topic | Image denoising Deep convolutional neural network Deep learning Noise reduction Image processing Adaptive white shark optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005690 |
| work_keys_str_mv | AT marycharlessheeba adaptivedeepresidualnetworkforimagedenoisingacrossmultiplenoiselevelsinmedicalnatureandsatelliteimages AT christopherseldevchristopher adaptivedeepresidualnetworkforimagedenoisingacrossmultiplenoiselevelsinmedicalnatureandsatelliteimages |