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The workflow involves: (1) acquiring a damaged input image from the Figshare database, (2) applying inpainting via a Context-Conditional Generative Adversarial Networks (CC-GAN) and a context encoder, both optimized using the JWWPA\u00a0\u2014 a hybrid of the Jaya algorithm and the Waterwheel Plant Algorithm (WWPA), and (3) combining the outputs through probabilistic function-based image fusion. The proposed JWWPA-Hybrid DL achieves a PSNR of 38.555 dB, SDME of 58.472[Formula: see text]dB, SSIM of 0.817, and UQI of 0.827, demonstrating superior performance compared to existing approaches. <\/jats:p>","DOI":"10.1142\/s0218001425520159","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:11:24Z","timestamp":1749773484000},"source":"Crossref","is-referenced-by-count":1,"title":["Jaya-Waterwheel Plant Algorithm with Hybrid Deep Learning and Probabilistic Fusion for Image Inpainting"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7497-0275","authenticated-orcid":false,"given":"T. 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