Image Processing Method Using Feature Transformation Network
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Solution Overview
Problem
Current face image synthesis methods fail to effectively combine identity and attribute features, leading to unrealistic image synthesis due to inadequate representation and joint constraint of network parameters during training, resulting in a lack of similarity to the expected attribute while maintaining content information.
Innovation Solution
An image processing method utilizing a feature transformation network with staged training, involving a content encoder network, attribute encoder network, feature transformation network, and decoder network, which performs feature fusion and mapping to generate an output image that maintains content information and resembles the expected attribute, using loss functions like L2 distance, content loss, and generative adversarial loss for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current face image synthesis methods are used to combine identity and attribute features, then the synthesis process can be completed, but the output images become unrealistic due to inadequate representation and joint constraint of network parameters during training
Solution Approach 1:
The patent divides the feature representation into separate content features (identity) and attribute features, extracting them from different input images through separate encoder networks. This segmentation allows independent optimization of each feature type while maintaining their relationship during synthesis, resolving the contradiction between feature combination accuracy and synthesis realism.
Solution Approach 2:
The patent introduces a feature transformation network as an intermediary that maps content features and attribute features into a shared latent space. This intermediary layer enables proper joint constraint of network parameters during training, allowing the synthesis system to learn realistic combinations of identity and attribute features that would otherwise be incompatible.
2Manufacturing precision
If feature fusion and mapping processing is performed to combine content and attribute features, then the output image can maintain content information and resemble expected attribute, but the training process becomes complex requiring multiple loss functions
Solution Approach 1:
The patent merges content features and attribute features through a unified feature transformation network that processes both feature types simultaneously. By combining the feature extraction, transformation, and synthesis operations into a single integrated system, the patent reduces overall complexity while maintaining high accuracy in representing both content and attribute information.
Solution Approach 2:
The feature transformation network serves multiple functions: it transforms content features, transforms attribute features, and integrates them into the final synthesis. This multi-functional design eliminates the need for separate training systems for each feature type, reducing complexity while achieving accurate representation of both content and attributes.
3Productivity
If staged training is used to train content encoder, attribute encoder, feature transformation network, and decoder network, then the training can be performed efficiently and quickly, but the system requires coordinated joint training with multiple loss functions
Solution Approach 1:
The patent implements preliminary action through staged training, where content encoder and attribute encoder are trained separately before integrating them into the full synthesis system. This preliminary training phase completes efficiently and quickly, establishing solid feature extraction capabilities before the more complex joint training with multiple loss functions begins.
Solution Approach 2:
The training process is segmented into distinct stages: independent training of content and attribute encoders, then integrated training of the feature transformation network and decoder. This segmentation allows efficient completion of each stage while managing the complexity of joint training through systematic progression.
Data Source
AI summary
An image processing method is provided. The image processing method includes: acquiring first second input images; extracting a content feature of the first input image; extracting an attribute feature of the second input image; performing feature fusion and mapping processing on the content feature of the first input image and the attribute feature of the second input image by using a feature transformation network to obtain a target image feature, the target image feature having the content feature of the first input image and the attribute feature of the second input image; and generating an output image based on the target image feature.