Image Domain Conversion Using Feature Distribution Correlation
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Solution Overview
Problem
Existing image processing methods using machine learning models often result in significant content distortion during domain conversion, leading to poor image processing effects.
Innovation Solution
An image processing method that involves training a model by calculating correlation degrees between image features and their distributions to adjust parameters in a way that increases similarity and reduces domain-specific information, using techniques like generative adversarial networks and cycle-consistent adversarial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional machine learning models are used for image domain conversion, then the processing speed is relatively fast, but the content distortion is severe and the image processing effect is poor
Solution Approach 1:
The patent implements feedback mechanisms through adversarial networks where the discriminator provides feedback to the generator about the authenticity of generated images. This feedback loop continuously refines the generated images to reduce content distortion and improve processing quality, addressing the contradiction between speed and quality in image domain conversion.
Solution Approach 2:
The patent introduces an intermediary feature correlation calculation mechanism that mediates between the source and target image domains. By calculating and optimizing feature correlations as an intermediary step, the system reduces content distortion while maintaining processing efficiency, resolving the contradiction between image processing quality and content preservation.
2Loss of information
If the image processing model is trained to maintain content similarity, then the content distortion is reduced, but the model training complexity increases
Solution Approach 1:
The patent segments the image processing task into multiple components: feature extraction, feature correlation calculation, and domain-specific transformation. This segmentation allows the model to maintain content similarity through dedicated feature correlation mechanisms while managing training complexity by dividing the overall task into manageable sub-tasks with specialized processing for each.
Solution Approach 2:
The patent transitions from traditional single-domain image processing to multi-dimensional feature space processing by calculating feature correlations across different domains. This dimensional transformation enables the model to capture content similarity in a higher-dimensional feature space, reducing content distortion while providing structured approaches to manage training complexity.
3Measurement precision
If feature correlation optimization is applied to reduce domain-specific information, then the image processing accuracy is improved, but the computational cost increases
Solution Approach 1:
The patent extracts and separately processes feature correlations from domain-specific information. By extracting the essential content features and calculating their correlations independently from domain-specific transformations, the system improves image processing accuracy while reducing unnecessary computational overhead associated with processing redundant domain-specific details.
Solution Approach 2:
The patent performs preliminary feature extraction and correlation calculation before the main image domain conversion process. By pre-computing feature correlations and storing them for reference during generation, the system improves processing accuracy while reducing the computational cost during the actual image conversion, as the expensive correlation calculations are performed in advance.
Data Source
AI summary
Methods and apparatuses for image processing are provided. A first image belonging to a first image domain is acquired and input to an image processing model to be trained to obtain a second image belonging to a second image domain. A first correlation degree between an image feature of the first image and an image feature of the second image to obtain a target feature correlation degree is calculated. A second correlation degree between feature value distribution of the image feature of the first image and feature value distribution of the image feature of the second image is calculated to obtain a distribution correlation degree. Model parameters of an image processing model are adjusted to a direction in which the target feature correlation degree is increased and a direction in which the distribution correlation degree is increased to obtain a trained image processing.