3D Image Modality Conversion via Constraint Loss Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image generative models, particularly generative adversarial networks (GANs), face challenges in accurately converting images between different modalities due to the difficulty in obtaining sample image groups with matching relationships, leading to low accuracy and poor performance in practical applications.
Innovation Solution
The method involves performing modality conversion and restoration on images using candidate networks to generate and restore three-dimensional images, with a constraint loss value calculated based on the conversion and restoration effects, allowing for improved training of the image conversion network by mapping images to a three-dimensional space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GAN network is trained using predetermined sample image groups with matching relationships, then the training process can be simplified, but the sample image group becomes difficult to obtain and the output accuracy deteriorates
Solution Approach 1:
The patent introduces a three-dimensional image space as an intermediate dimension for training. Instead of directly mapping between two different modalities in two-dimensional space, the method maps the first modality image to a three-dimensional image space, then to the second modality image. This additional dimensional space facilitates the training process while maintaining or improving output accuracy, resolving the contradiction between training simplicity and output precision.
2Productivity
If GAN network is trained with predetermined sample image groups, then training can proceed, but the large difference between different modality images results in poor model performance
Solution Approach 1:
The three-dimensional image space serves as an intermediary between the first and second modality images. This intermediate representation helps bridge the large differences between different modalities by providing a common space where features can be aligned and compared, thereby improving model performance while maintaining training capability.
Solution Approach 2:
By introducing the three-dimensional image space, the patent creates an additional dimension that helps accommodate the differences between modalities. This dimensional expansion provides more flexibility in representing and transforming images across different modalities, improving both training capability and model performance.
3Adaptability or versatility
If conventional GAN training is used, then the process can be implemented with existing methods, but the accuracy of output results deteriorates due to large differences between modality images
Solution Approach 1:
The patent maintains compatibility with conventional GAN training methods while improving accuracy by introducing a three-dimensional image space. The training process still uses candidate networks and loss functions similar to existing GAN approaches, but the addition of the third dimension enhances the representation capacity and transformation accuracy between different modalities.
Solution Approach 2:
The three-dimensional image space acts as a mediator that enables more accurate transformations between different modalities. This intermediate space allows for better feature alignment and transformation while maintaining compatibility with existing GAN training frameworks, thus improving output accuracy without sacrificing method adaptability.
Data Source
AI summary
An image generation method includes obtaining a modality image corresponding to a first modality, and performing modality conversion on the modality image through a first candidate network to obtain a generated image corresponding to a second modality different from the first modality. The generated image is a three-dimensional image. The method further includes performing modality restoration on the generated image through a second candidate network to obtain a restored image corresponding to the first modality and obtaining a constraint loss value based on a modality conversion effect of the generated image and a modality restoration effect of the restored image. The constraint loss value indicates a mapping loss in mapping the modality image to a three-dimensional image space by the first candidate network. The method also includes training the first candidate network based on the constraint loss value to obtain an image conversion network.