Revitalizing Underwater Image Enhancement By Using Machine Learning Technique Fault Detection In Industrial Systems
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2025
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| _version_ | 1866902120572125184 |
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| author | Vaishnavi, Randale Shastri, Dr.Swaroopa |
| author_facet | Vaishnavi, Randale Shastri, Dr.Swaroopa |
| contents | <p>Sub-sea image recovery is challenging because water has special optical properties, including absorption and<br>scattering, that compromise the quality of the image. The captured images and videos frequently suffer from two displeasing<br>problems: First, color distortion; and second, poor visibility. This is mainly because that the light is exponentially attenuated<br>while penetrating through water and the strength of attenuation is color dependent. This study introduces a hybrid unsupervised method for underwater image restoration, integrating Support Vector Machines (SVM) with traditional Decision Tree algorithm.<br>The suggested approach utilizes the capabilities of SVM in classification to improve the performance of Decision Tree methods<br>in the restoration process. SVM is used in this approach to classify different underwater environments and conditions to facilitate<br>more accurate utilization of restoration methods appropriate for each class. Decision Tree algorithm, in contrast, adjusts<br>restoration parameters dynamically using the classifications given by the SVM. This hybrid model aims to improve color<br>correction, contrast enhancement, and visibility restoration in underwater imagesResults show that the hybrid SVM and<br>Decision Tree algorithm method surpasses classic Decision Tree algorithms based on visual quality and quantitative measures<br>e.g., Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The comparison of a SVM And Decision Tree<br>Algorithm, The SVM accuracy is 92% and The Decision Tree Algorithm 98%,The highest accuracy is Decision Tree algorithm.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16742907 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Revitalizing Underwater Image Enhancement By Using Machine Learning Technique Fault Detection In Industrial Systems Vaishnavi, Randale Shastri, Dr.Swaroopa Underwater image restoration, Machine Learning, SVM, Decision Tree algorithm <p>Sub-sea image recovery is challenging because water has special optical properties, including absorption and<br>scattering, that compromise the quality of the image. The captured images and videos frequently suffer from two displeasing<br>problems: First, color distortion; and second, poor visibility. This is mainly because that the light is exponentially attenuated<br>while penetrating through water and the strength of attenuation is color dependent. This study introduces a hybrid unsupervised method for underwater image restoration, integrating Support Vector Machines (SVM) with traditional Decision Tree algorithm.<br>The suggested approach utilizes the capabilities of SVM in classification to improve the performance of Decision Tree methods<br>in the restoration process. SVM is used in this approach to classify different underwater environments and conditions to facilitate<br>more accurate utilization of restoration methods appropriate for each class. Decision Tree algorithm, in contrast, adjusts<br>restoration parameters dynamically using the classifications given by the SVM. This hybrid model aims to improve color<br>correction, contrast enhancement, and visibility restoration in underwater imagesResults show that the hybrid SVM and<br>Decision Tree algorithm method surpasses classic Decision Tree algorithms based on visual quality and quantitative measures<br>e.g., Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The comparison of a SVM And Decision Tree<br>Algorithm, The SVM accuracy is 92% and The Decision Tree Algorithm 98%,The highest accuracy is Decision Tree algorithm.</p> |
| title | Revitalizing Underwater Image Enhancement By Using Machine Learning Technique Fault Detection In Industrial Systems |
| topic | Underwater image restoration, Machine Learning, SVM, Decision Tree algorithm |
| url | https://doi.org/10.5281/zenodo.16742907 |