Super Resolution Fidelity Transfer Neural Network
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Solution Overview
Problem
Current methods for image super-resolution focus primarily on style transfer and do not effectively enhance the resolution or fidelity of visual data, lacking a comprehensive approach to incorporate high-frequency features and content features for improved image quality.
Innovation Solution
A computer-implemented method using neural networks to receive low-resolution visual data and fidelity data, extracting content and high-frequency features to produce high-resolution visual data by optimizing a cost function that combines squared errors between extracted features, enabling the transfer of high-frequency components from fidelity data to enhance image fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current style transfer methods are used for super-resolution, then the style of the image can be enhanced, but the resolution and fidelity of the visual data are not effectively improved
Solution Approach 1:
The patent merges style transfer methods with super-resolution techniques by combining style loss functions with high-frequency feature extraction. This integration allows the system to simultaneously enhance both the stylistic quality and the resolution/fidelity of visual data, resolving the contradiction between style enhancement and fidelity improvement.
Solution Approach 2:
The patent creates a multi-functional system that can perform both style transfer and super-resolution enhancement through a unified framework. By incorporating multiple loss functions (style loss, content loss, high-frequency loss) into a single optimization process, the method achieves versatile functionality that addresses both stylistic enhancement and fidelity improvement.
2Manufacturing precision
If high-frequency features are extracted from fidelity visual data, then image resolution and quality are improved, but computational complexity increases
Solution Approach 1:
The patent segments the feature extraction process into distinct components: content feature extraction using activations from different layers, and high-frequency feature extraction using Laplacian pyramids. This segmentation allows each component to be optimized independently, managing computational complexity while achieving high-resolution enhancement.
Solution Approach 2:
The patent transforms the super-resolution problem from a direct spatial domain approach to a multi-dimensional feature space approach. By extracting features from multiple neural network layers and combining them with high-frequency Laplacian pyramid representations, the system operates in an expanded feature dimensionality that enables more efficient optimization.
3Measurement precision
If multiple neural networks are used for feature extraction, then feature accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary feature extraction by obtaining activations from pre-trained neural network layers before the main optimization process. By pre-computing and storing these intermediate features, the system avoids redundant calculations during the super-resolution optimization, reducing processing time while maintaining feature extraction accuracy.
Solution Approach 2:
The patent uses pre-trained neural network models (such as VGG networks) whose weight matrices are copied and utilized for feature extraction. This approach leverages existing trained models to extract accurate features without requiring training new networks from scratch, significantly reducing computational resources and training time.
Data Source
Figure 1
AI summary
A method for developing improved-fidelity visual data using fidelity data and using a hierarchical algorithm, the method comprising the steps of: receiving at least a plurality of neighbouring sections of visual data; selecting a plurality of input sections from the received plurality of neighbouring sections of visual data; extracting features from the plurality of input sections of visual data; and producing the improved-fidelity visual data by applying the fidelity data to the extracted features.