AI Image Generation Using Anti-Neural Network and Detail Completion
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Solution Overview
Problem
Current AI systems lack an effective method for generating high-quality images, which is a time-consuming and labor-intensive task even for professional artists, and there is a need for an AI-based image generation system that can produce realistic images efficiently.
Innovation Solution
The proposed method involves acquiring and preprocessing a diverse set of original images, training an anti-neural network model using Progressive Growing of Generative Adversarial Net (PGGAN), and utilizing a detail completion network to enhance the generated images to match a specified style, thereby producing high-quality, realistic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image creation is used, then image quality can be high, but time consumption and labor intensity are excessive
Solution Approach 1:
The patent uses GANs to copy and replicate the characteristics of real images. The generator network learns to copy the distribution of real images from the training dataset, while the discriminator network verifies the authenticity of copied images. This enables automated generation of high-quality images without manual creation, resolving the contradiction between productivity and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual image creation process with an automated AI system. Instead of artists manually creating images, the system uses neural networks (generator and discriminator) that automatically learn from training data and generate images, substituting human mechanical labor with computational processes.
2Productivity
If AI image generation is implemented, then productivity increases, but image quality and realism may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism through the discriminator network that evaluates generated images and provides feedback to the generator. The discriminator acts as a critic that identifies fake images, forcing the generator to improve its output quality. This adversarial feedback loop continues until the generator produces highly realistic images that can fool the discriminator, thereby maintaining high image quality while achieving automated generation.
Solution Approach 2:
The patent performs preliminary training of the GAN model using large datasets of real images before actual image generation. The generator and discriminator are pre-trained together, with the discriminator learning to distinguish real from fake images and the generator learning to produce realistic images. This preliminary training ensures the system has the capability to generate high-quality images when deployed for actual image generation tasks.
Data Source
AI summary
An image generation method and a computing device employing the method includes: acquiring a plurality of original images; and processing the plurality of original images to obtain a training data set. An anti-neural network model is trained according to the training data set. A candidate image is generated through the trained anti-neural network model. The candidate image is complemented through a detail completion network model to obtain a target image according to a comparison image. Thereby, a style of the generated image is the same as that of the comparison image. A more realistic image can be randomly generated saving the time and energy of artificially creating an image.