A Review of Image Inpainting: Deep Generative Models and Data Mining Applications

The field of image inpainting, which involves replacing damaged or absent areas in photographs with realistic material, has made great strides recently, especially with the introduction of deep learning techniques. This study offers a thorough analysis of current inpainting research on images, with an emphasis on applying data mining approaches to improve the robustness and performance of inpainting models. The introduction section outlines the transition from traditional image inpainting methods to deep learning solutions, leveraging CNNs and GANs for enhanced performance. The literature review covers diverse applications such as transportation systems and ancient book preservation, showcasing advancements in deep generative models. Additionally, the background section underscores the importance of image forensics for transparency and integrity verification. Data mining techniques, including classification and anomaly detection, are explored for their applicability in image inpainting. Further discussion continues into data mining solutions for inpainting, detailing dataset selection, preprocessing, and the utilization of advanced machine learning algorithms for generating high- quality inpaintings. All things considered, this study advances the field of image inpainting research by offering a thorough summary of current developments and data mining methods in improving the robustness and performance of inpainting models.