Generative Recurrent Neural Network for Image Enhancement and Training Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing artificial intelligence-based image enhancement models face challenges in generating diverse training data, especially in special environments, due to the complexity and resource-intensive nature of training these models.
Innovation Solution
A method and apparatus that integrate image enhancement and training data generation using a generative recurrent neural network model, which can generate various types of low-quality images from high-quality inputs and enhance these images through a trained model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image enhancement models are used, then image quality can be enhanced, but diverse training data generation is difficult especially in special environments
Solution Approach 1:
The system performs preliminary action by generating diverse low-quality training images from high-quality reference images using a generative recurrent neural network before actual training is needed. This pre-generation of training data in various special environments (low light, night, fog, etc.) ensures that sufficient diverse training data is available without requiring actual collection in those difficult environments.
Solution Approach 2:
The system creates copies by generating synthetic low-quality images that replicate the characteristics of real low-quality images obtained in special environments. The generative model produces realistic training samples that copy the visual degradation patterns (noise, blur, artifacts) without requiring physical access to those environments, thus solving the data scarcity problem.
2Manufacturing precision
If AI-based image enhancement models become more complex to improve performance, then enhancement quality improves, but training time and data requirements increase rapidly
Solution Approach 1:
The system merges two functions into a single integrated model: image enhancement and training data generation. The generative recurrent neural network simultaneously performs both tasks, eliminating the need for separate training processes. This consolidation reduces overall training time while maintaining high enhancement quality, as the model learns both to enhance images and to generate realistic training data in one unified training framework.
3Measurement precision
If sufficient diverse training data is required to ensure model performance, then enhancement accuracy improves, but securing training data in special environments becomes very difficult or impossible
Solution Approach 1:
The system creates copies by generating synthetic low-quality images that replicate the characteristics of real low-quality images obtained in special environments. The generative model produces realistic training samples that copy the visual degradation patterns (noise, blur, artifacts) without requiring physical access to those environments, thus solving the data scarcity problem.
Solution Approach 2:
The generative recurrent neural network acts as an intermediary that bridges the gap between high-quality reference images and the needed low-quality training data. Instead of directly collecting difficult-to-obtain real low-quality images from special environments, the system uses the generative model as an intermediate step to synthesize realistic training samples that preserve the essential characteristics needed for accurate enhancement.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed are a method and apparatus for integrating image enhancement and training data generation using a generative recurrent neural network model. The method of integrating image enhancement and training data generation using a generative recurrent neural network model includes: (a) receiving a target image as input, and (b) applying the target image to a trained generative recurrent neural network model to selectively generate any one of a high-quality image with enhanced image quality and a low-quality image depending on a type of the target image.