Seamless Image Restoration Using Inpainting Algorithm: A Deep Learning Approach

Image restoration is a key use of computer vision where the missing or lost parts of an image are replaced while maintaining visual coherence. Inpainting by classical techniques falls short when it comes to wide gaps and intricate patterns, which leads to inconsistencies and artifacts. This work suggests a deep learning-based inpainting technique based on Convolutional Neural Networks and Generative Adversarial Networks to enhance image restoration accuracy. The model is designed to preserve high-quality fine details, contextual integrity, and improved overall visual quality. Through cutting-edge feature extraction, the system achieves enhanced reconstruction outcomes on various datasets. The use of the evaluation metrics, such as the Peak Signal-to-Noise Ratio, the Structural Similarity Index, confirms the effectiveness of the approach. The method can be implemented in practice for image enhancement, restoration of old photos, and medical imaging, providing a cost-effective and scalable framework for image reconstruction.