Face Image Recovery Model Using Semantic Feature Transfer
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Solution Overview
Problem
Existing face image recovery methods ignore semantic information, leading to inconsistent recovery results and poor generalization performance, especially in real-world scenarios with degraded images.
Innovation Solution
A face image recovery method based on semantic features, utilizing a recovery model with an encoder, reference image generator, feature transfer, and decoder to generate high-quality face images by preserving semantic information and improving texture and detail recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing face recovery methods focus on geometric structure and texture recovery, then the visual quality of recovered images is improved, but semantic information consistency deteriorates
Solution Approach 1:
The patent segments the face recovery process into distinct modules: an encoder for extracting semantic features, a reference image generator for creating high-quality references, a feature transfer module for transferring semantic information, and a decoder for reconstructing the final image. This segmentation allows each module to specialize in specific tasks, ensuring both semantic consistency and texture quality.
Solution Approach 2:
The patent introduces a reference image generator as an intermediary component that creates high-quality reference images from semantic features. This intermediary bridge connects the semantic feature extraction and the final image reconstruction, ensuring that semantic information is preserved while generating realistic textures and details.
2Ease of manufacture
If pre-constructed dictionaries are used in reference methods, then the recovery process is simplified, but generalization performance deteriorates in real degraded images
Solution Approach 1:
The patent replaces static pre-constructed dictionaries with a dynamic reference image generator that adapts to different degraded face images in real-time. The generator creates reference images based on the specific input image's semantic features, allowing the system to adapt to various degradation types and conditions without requiring pre-defined dictionaries.
Solution Approach 2:
The patent changes the parameter approach from fixed pre-constructed dictionaries to dynamic parameter generation. The reference image generator uses semantic features as parameters to synthesize appropriate reference images, enabling the system to adjust to different face types, expressions, and degradation levels dynamically.
3Stability of the object's composition
If geometric a priori methods are used, then the recovery process is constrained by prior knowledge, but effective geometric information capture from low-quality images deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the encoder continuously refines semantic feature extraction from the degraded input image, and the reference image generator uses these features to create adaptive references. This feedback loop allows the system to capture geometric information iteratively, improving accuracy without being constrained by fixed prior knowledge.
Data Source
AI summary
The present application discloses a method for recovering a face image based on semantic features, which includes obtaining a to-be-recovered face image; inputting the to-be-recovered face image into a recovery model trained to obtain a recovered face image; the recovery model includes: an encoder, configured for generating low-quality face semantic features based on the to-be-recovered face image; a reference image generator, configured for generating a plurality of high-quality face reference images by inputting random noise based on the low-quality face semantic features; a feature transfer, configured for constructing a face component feature dictionary based on the plurality of high-quality face reference images, and transferring high-quality component features in the face component feature dictionary to the low-quality face semantic features to obtain high-quality face semantic features; and a decoder, configured for generating the recovered face image based on the high-quality face semantic features.