Region-Specific Image Generation Learning for Edge Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image generation methods using neural networks require extensive training time and may not adequately improve the quality of specific image portions, such as edges, leading to inefficiencies in image conversion processes.
Innovation Solution
A system and method that adjusts learning parameters to suppress errors between input and converted images, focusing on specific regions of an image through different learning techniques, including region division and weight assignment to loss functions, to enhance the quality of critical image portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If learning is performed to match the entire images using neural networks, then the overall image quality is improved, but a large amount of time is required for model generation
Solution Approach 1:
The image is divided into multiple regions (first region and second region) with different characteristics. Different learning models or different learning parameters are applied to different regions, allowing the system to focus computational resources on critical areas while reducing overall processing time.
Solution Approach 2:
Different learning approaches are applied to different regions of the image based on their specific requirements. The first region receives one type of learning treatment while the second region receives another, optimizing both quality and efficiency for each region's specific needs.
Data Source
AI summary
A system for performing efficient learning of a specific portion is provided. The system generates a converted image on the basis of input of an input image, the system including a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.