Abstract

This research introduces an advanced object removal tool addressing the challenges in computer vision and image processing. Leveraging Generative Adversarial Networks (GANs) with Python, TensorFlow, and OpenCV, the tool utilizes conditional GANs for efficient learning of patterns and structures. By employing matched image sets comprising original images and corresponding object masks, the program generates realistic and aesthetically pleasing in-painted images. Additionally, a user-friendly web application is developed, allowing users to select input images and define specific regions for removal. The tool's versatility extends to video editing, incorporating a Spatial-Temporal Transformer Network (STTN) for seamless removal of objects in both spatial and temporal dimensions. This comprehensive solution enhances user engagement and expands the tool's applicability to eliminating unwanted elements from videos