GAN Face Image Correction via Mask Synthesis
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Solution Overview
Problem
Conventional image editing programs require technical knowledge and experience to correct images realistically, especially when using 3D model engines, making it difficult for general users to achieve realistic results.
Innovation Solution
A face image correction system utilizing generative adversarial networks (GANs) that preprocesses images to generate mask, sketch, and color inputs, allowing users to easily correct images by predicting and synthesizing new images without needing separate image tools or expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing programs or 3D model engines are used, then image correction functionality is provided, but technical knowledge and experience are required making operation difficult for general users
Solution Approach 1:
A neural network model acts as an intermediary between the user and the complex image editing system. The user provides simple input images, and the neural network automatically performs the complex editing operations that would otherwise require expert knowledge and manual manipulation of multiple tools and parameters.
Solution Approach 2:
The system performs automatic image correction without requiring user intervention in the editing process. The neural network autonomously analyzes the input image, determines appropriate corrections, and generates the corrected output image, eliminating the need for users to manually adjust parameters or understand editing techniques.
2Adaptability or versatility
If 3D model engines are used for image editing, then advanced editing capabilities are achieved, but multiple different engines with varying knowledge requirements create system complexity
Solution Approach 1:
The neural network model serves as a universal image editing system that can perform multiple types of corrections (beautification, restoration, artistic effects, etc.) through a single unified architecture. This eliminates the need for multiple specialized 3D model engines, each requiring different knowledge and configuration.
Solution Approach 2:
The system uses a single neural network model with adjustable parameters and weights that can be fine-tuned for different editing tasks. Instead of switching between multiple engines, the same model adapts to different editing requirements through parameter adjustments and different input configurations.
3Manufacturing precision
If manual image correction is performed by general users, then image editing is attempted, but insufficient technical knowledge results in unrealistic and awkward corrected images
Solution Approach 1:
The patent replaces manual mechanical editing operations with an automated neural network system. The neural network learns from training data what realistic corrections look like and automatically applies them, substituting the need for human expertise and manual adjustment with an intelligent automated system that consistently produces realistic results.
Data Source
AI summary
An image correcting method of the present invention includes: a step of performing a preprocessing process on an original image to generate a mask image including only an erased area of the original image; a step of predicting, by using generative adversarial networks, an image which is to be synthesized with the erased area in the mask image; and a step of synthesizing the predicted image with the erased area of the original image to generate a new image.