Blind Face Restoration via Pyramid Feature Segmentation
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Solution Overview
Problem
Existing blind face restoration (BFR) methods using generative adversarial networks (GANs) struggle to maintain delicate facial features and ensure identity preservation when generating high-quality face images from low-quality inputs.
Innovation Solution
A processor-implemented method that involves obtaining a pyramid feature of an input face image, generating style features at multiple levels based on integrated features, latent codes, and noise, and then using these features to generate a high-quality face image that preserves facial identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a pre-trained GAN model is used to generate realistic textures, then the quality of generated face images is improved, but the delicate facial features and identity of the input image are lost
Solution Approach 1:
The method segments the feature extraction process into multiple pyramid levels, where each level processes different frequency components of facial features. The high-level features capture global identity while low-level features preserve detailed textures, allowing both identity preservation and quality enhancement simultaneously
Solution Approach 2:
The method applies different processing strategies to different regions and levels of the pyramid structure. High-level features undergo style transfer for quality improvement, while low-level features are preserved more faithfully to maintain delicate facial details and identity characteristics
2Manufacturing precision
If higher fidelity is obtained in generated images, then the quality is improved, but the generated image becomes less smooth
Solution Approach 1:
The method introduces a multi-level pyramid dimension to the feature processing, where features are represented at multiple scales and resolutions. This dimensional expansion allows the system to balance fidelity and smoothness by integrating information across different levels, achieving high fidelity while maintaining overall image smoothness through proper feature fusion
Data Source
AI summary
A processor-implemented method including obtaining a pyramid feature of an input face image, generating a first initial style feature corresponding to a highest level feature based on the highest level feature of the pyramid feature, and a latent code and noise of the input face image, generating a first style feature corresponding to each level feature based on an integrated feature corresponding to each level feature of the pyramid feature, the noise, and the latent code, and generating a high-quality face image of the input face image based on the first initial style feature and the first style feature corresponding to each level feature of the pyramid feature, the integrated feature corresponding to each level feature is generated based on each level feature, the first style feature corresponding to a higher level feature of each level feature, the latent code, and the noise and an integrated feature corresponding to the highest level feature of the pyramid feature is generated based on the highest level feature, the latent code, the noise, and the first initial style feature.