International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Restoring the Past: an Ai-driven Approach to Image Enhancement and Repair

Author(s) Mr. HARISH M, Ms. ANANYA K, Ms. NIVEDITA GOWDA, Mr. SURAJ V, Mr. ADITYA S
Country India
Abstract The increasing need for preserving visual data, enhancing surveillance clarity, and restoring old or damaged imagery highlights the demand for AI-driven image enhancement systems. Restoring the Past: An AI-Driven Approach to Image Enhancement and Repair is an intelligent framework that integrates deep learning models such as GFPGAN and Real-ESRGAN to improve image and video quality while maintaining visual authenticity. The system effectively addresses challenges like noise, blur, and low resolution in degraded media using adaptive restoration techniques. It further incorporates real-time face detection and recognition modules that accurately identify individuals even in low-light or noisy environments. A built-in notification system alerts authorized users instantly upon identification of known or unknown faces. By combining restoration, recognition, and alert mechanisms in one framework, the system ensures both visual clarity and timely response for applications in surveillance, forensics, and digital media recovery. This AI-powered approach demonstrates high efficiency, accuracy, and adaptability, making it a robust solution for improving degraded visual content and supporting automated, intelligent image repair. The proposed approach demonstrates high efficiency, adaptability, and practical accuracy, achieving an overall performance level of 85%.
Keywords Image enhancement, video restoration, face recognition, GFPGAN, Real-ESRGAN, OpenCV, adaptive AI systems
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-05
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62504
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E-ISSN 2582-2160
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