AI Image Editing With Text-Guided Object Generation and Removal

The system segments the image modification task into distinct functional modules: a CNN-based object recognition module for detecting and classifying objects in the image, and a GAN-based generation module for adding, removing, or modifying objects. This segmentation allows each module to specialize in specific functions, improving overall productivity while managing complexity through modular architecture.

The electronic device implements a universal image modification system that can perform multiple operations (adding objects, removing objects, modifying existing objects) through a single integrated AI framework. The GAN model serves multiple purposes by functioning as both a generator for creating new objects and a discriminator for evaluating modification quality, reducing the need for separate specialized systems.

The system introduces a discriminator network as an intermediary component that mediates between the generator and the final output. The discriminator evaluates the authenticity of generated images and provides feedback to the generator, ensuring high modification accuracy. This intermediary mechanism enables precise control over image modification quality while maintaining a structured approach to managing the complexity of the GAN architecture.

The GAN implementation incorporates continuous feedback loops where the discriminator network evaluates generated images and provides error signals back to the generator for iterative improvement. This feedback mechanism ensures high manufacturing precision in image modifications by constantly refining the generated content, while the feedback-driven training process manages model complexity through structured optimization.

The system implements self-service automation where the AI models automatically perform object recognition, classification, and modification operations without requiring manual intervention for each editing task. The CNN automatically detects and classifies objects, and the GAN automatically generates and refines modified images, dramatically improving productivity while the automated nature of these processes is managed through standardized algorithms to control complexity.

The patent replaces manual mechanical image editing processes with automated AI-based systems. The CNN and GAN models substitute human operators in performing object detection, classification, and image manipulation tasks, achieving high productivity through automated computation while managing complexity through established machine learning frameworks and algorithms.