Neural Diffusion Image Processing with Object-Specific Randomness Control
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Solution Overview
Problem
Current image processing technologies using neural networks face challenges in effectively preserving and enhancing specific object characteristics in output images, particularly in controlling randomness levels for each object within an image, which affects the accuracy and quality of the denoising process.
Innovation Solution
The method involves setting a randomness level for target objects, generating noise images through a diffusion process, extracting and saving partial preservation areas from noise images based on this level, and applying these areas to denoise images using a neural diffusion model to produce denoised output images that balance characteristics of the guide and target domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a diffusion process is used to generate noise images for image processing, then the ability to transform between guide domain and target domain is improved, but the randomness level becomes difficult to control for specific objects
Solution Approach 1:
The patent applies local quality by setting different randomness levels for different objects within the same image. The processor identifies multiple objects in the guide image and assigns specific randomness levels to each object individually, allowing precise control over which parts of the image retain guide domain characteristics versus target domain characteristics during the diffusion process.
Solution Approach 2:
The patent segments the guide image into multiple objects and processes each object separately with its own randomness level. This segmentation allows the system to handle different objects differently, preserving important characteristics in some objects while allowing more transformation in others, thereby resolving the contradiction between adaptability and precision control.
2Measurement precision
If guide domain characteristics are preserved in the output image, then the accuracy of target object representation is improved, but the expression of target domain characteristics is weakened
Solution Approach 1:
The patent uses parameter changes by adjusting the randomness level parameter for different objects to control the balance between guide domain and target domain characteristics. By varying this parameter, the system can achieve different degrees of preservation versus transformation for each object, allowing optimization of both accuracy and adaptability simultaneously.
Data Source
AI summary
A method and apparatus with image processing based on neural diffusion are provided. The method includes: setting a randomness level for a target object; generating a noised image by performing a diffusion process of generating noise images while repeatedly performing noising based on a guide image of a guide domain including the target object and by extracting and saving, based on the randomness level, a partial preservation area from a noise image among the noise images; and obtaining a denoised output image of a target domain by performing a reverse process of repeatedly generating, based on the noised image, denoise images corresponding to the noise images and by applying the saved partial preservation area to a denoise image among the denoise images.