A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images
Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/2/51 |
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| author | Milan Tripathi Waree Kongprawechnon Toshiaki Kondo |
| author_facet | Milan Tripathi Waree Kongprawechnon Toshiaki Kondo |
| author_sort | Milan Tripathi |
| collection | DOAJ |
| description | Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. To mitigate information loss during encoding, skip connections transfer essential feature maps from the encoder to the decoder, preserving structural details. However, skip connections can also propagate redundant information. To address this, we incorporate attention gates within the skip connections, ensuring that only relevant features are passed to the decoding layers. We evaluate the robustness of the proposed method across facial, medical, and remote sensing domains. The experimental results demonstrate that HREDN excels in preserving edge details and structural features in denoised images, outperforming state-of-the-art techniques in both qualitative and quantitative measures. Statistical analysis further highlights the model’s ability to effectively remove noise in diverse, complex scenarios with images of varying resolutions across multiple domains. |
| format | Article |
| id | doaj-art-1d80c095d07c4369b0bb02cc33c800b0 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-1d80c095d07c4369b0bb02cc33c800b02025-08-20T03:12:22ZengMDPI AGJournal of Imaging2313-433X2025-02-011125110.3390/jimaging11020051A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital ImagesMilan Tripathi0Waree Kongprawechnon1Toshiaki Kondo2School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, ThailandSchool of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, ThailandSchool of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, ThailandImage denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. To mitigate information loss during encoding, skip connections transfer essential feature maps from the encoder to the decoder, preserving structural details. However, skip connections can also propagate redundant information. To address this, we incorporate attention gates within the skip connections, ensuring that only relevant features are passed to the decoding layers. We evaluate the robustness of the proposed method across facial, medical, and remote sensing domains. The experimental results demonstrate that HREDN excels in preserving edge details and structural features in denoised images, outperforming state-of-the-art techniques in both qualitative and quantitative measures. Statistical analysis further highlights the model’s ability to effectively remove noise in diverse, complex scenarios with images of varying resolutions across multiple domains.https://www.mdpi.com/2313-433X/11/2/51image denoisingGaussiansalt-and-pepperencoder–decodermulti-scale feature enhancement |
| spellingShingle | Milan Tripathi Waree Kongprawechnon Toshiaki Kondo A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images Journal of Imaging image denoising Gaussian salt-and-pepper encoder–decoder multi-scale feature enhancement |
| title | A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images |
| title_full | A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images |
| title_fullStr | A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images |
| title_full_unstemmed | A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images |
| title_short | A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images |
| title_sort | highly robust encoder decoder network with multi scale feature enhancement and attention gate for the reduction of mixed gaussian and salt and pepper noise in digital images |
| topic | image denoising Gaussian salt-and-pepper encoder–decoder multi-scale feature enhancement |
| url | https://www.mdpi.com/2313-433X/11/2/51 |
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