Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction
Abstract Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel i...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82242-9 |
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| author | Gamal M. Mahmoud Wael Said Magdy M. Fadel Mostafa Elbaz |
| author_facet | Gamal M. Mahmoud Wael Said Magdy M. Fadel Mostafa Elbaz |
| author_sort | Gamal M. Mahmoud |
| collection | DOAJ |
| description | Abstract Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. The intelligent sperm motility heuristic navigates the image’s pixel space, identifying the most influential neighboring pixels for accurate imputation. Our approach includes three essential modifications: (1) integration of an identity module within the GAN architecture to mitigate the vanishing gradient problem; (2) introduction of a metaheuristic algorithm based on sperm motility to select the top 10 pixels that most effectively contribute to the generation of the missing pixel; and (3) the implementation of an adaptive interval mechanism between the discriminator’s actual value and the weighted average of the selected pixels, enhancing the generator’s efficiency and ensuring the coherence of the imputed pixels with the surrounding image context. We evaluate the proposed method on three distinct datasets (Energy Images, NREL Solar Images, and NREL Wind Turbine Dataset), demonstrating its superior performance in maintaining pixel integrity during the imputation process. Our experiments also confirm the approach’s effectiveness in addressing everyday challenges in GANs, such as mode collapse and vanishing gradients, across various GAN architectures. |
| format | Article |
| id | doaj-art-1d65eab4b1f542759b52a74ebac38aa6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1d65eab4b1f542759b52a74ebac38aa62025-01-12T12:23:53ZengNature PortfolioScientific Reports2045-23222025-01-0115112210.1038/s41598-024-82242-9Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstructionGamal M. Mahmoud0Wael Said1Magdy M. Fadel2Mostafa Elbaz3Department of Electrical Engineering, Pharos University in AlexandriaComputer Science Department, Faculty of Computers and Informatics, Zagazig UniversityComputer Engineering and Systems Department, Faculty of Engineering, Mansoura UniversityDepartment of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh UniversityAbstract Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. The intelligent sperm motility heuristic navigates the image’s pixel space, identifying the most influential neighboring pixels for accurate imputation. Our approach includes three essential modifications: (1) integration of an identity module within the GAN architecture to mitigate the vanishing gradient problem; (2) introduction of a metaheuristic algorithm based on sperm motility to select the top 10 pixels that most effectively contribute to the generation of the missing pixel; and (3) the implementation of an adaptive interval mechanism between the discriminator’s actual value and the weighted average of the selected pixels, enhancing the generator’s efficiency and ensuring the coherence of the imputed pixels with the surrounding image context. We evaluate the proposed method on three distinct datasets (Energy Images, NREL Solar Images, and NREL Wind Turbine Dataset), demonstrating its superior performance in maintaining pixel integrity during the imputation process. Our experiments also confirm the approach’s effectiveness in addressing everyday challenges in GANs, such as mode collapse and vanishing gradients, across various GAN architectures.https://doi.org/10.1038/s41598-024-82242-9Pixel imputationGANsIdentity blockIntelligent sperm attitudeEnergy source imagesSolar fault detection |
| spellingShingle | Gamal M. Mahmoud Wael Said Magdy M. Fadel Mostafa Elbaz Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction Scientific Reports Pixel imputation GANs Identity block Intelligent sperm attitude Energy source images Solar fault detection |
| title | Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction |
| title_full | Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction |
| title_fullStr | Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction |
| title_full_unstemmed | Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction |
| title_short | Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction |
| title_sort | novel gsip gan based sperm inspired pixel imputation for robust energy image reconstruction |
| topic | Pixel imputation GANs Identity block Intelligent sperm attitude Energy source images Solar fault detection |
| url | https://doi.org/10.1038/s41598-024-82242-9 |
| work_keys_str_mv | AT gamalmmahmoud novelgsipganbasedsperminspiredpixelimputationforrobustenergyimagereconstruction AT waelsaid novelgsipganbasedsperminspiredpixelimputationforrobustenergyimagereconstruction AT magdymfadel novelgsipganbasedsperminspiredpixelimputationforrobustenergyimagereconstruction AT mostafaelbaz novelgsipganbasedsperminspiredpixelimputationforrobustenergyimagereconstruction |