This project primarily focuses on the restoration of damaged or occluded images by employing region-based pixel interpolation methods to recover missing visual data with high fidelity. Image inpainting, a critical task in the fields of computer vision,3 that aims to restore the missing or corrupted parts in an image while maintaining its overall visual data. However, traditional image inpainting methods often struggle to effectively preserve fine details, structural information, and the overall context of the image, particularly when faced with large missing data. The proposed approach addresses these challenges by using advanced pixel interpolation algorithms in combination with region-based segmentation techniques. By guiding the interpolation algorithms with region-specific missing data, the method enhances both local detail preservation and global structural coherence, enabling effective recovery of missing visual data. To achieve this, the project explores a range of interpolation techniques, including bilinear, bicubic, gradient-based, and edge-guided interpolation, which are further optimized to work within segmented regions. The primary objectives of this project are to restore missing or occluded areas of an image, preserve fine details and textures, and ensure seamless integration between restored regions and surrounding undamaged areas. The methodology prioritizes the alignment of newly generated pixels with the structural and contextual features of the original image, ensuring that the restoration is both accurate, mathematically and visually convincing. This research has broad applications across various domains, such as restoring old or damaged photographs. By addressing the limitations of existing inpainting techniques, the project advances the field of image processing and contributes practical solutions for visual data recovery across diverse contexts.