Diffusion Image Processing for Pixel-Wise Artifact Correction
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Solution Overview
Problem
Existing digital image synthesis methods struggle to effectively identify and mitigate natural and unnatural artifacts in synthetic images generated from text-to-image diffusion models, limiting their quality and usability.
Innovation Solution
A computer-implemented method and device that utilize a text-to-image diffusion process with a backward denoising mechanism to determine pixel-wise gradients, identify artifacts, and iteratively refine synthetic images using random noise replacement until desired quality metrics are met, while also training for anomaly detection in real-world components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text-to-image diffusion models are used to generate synthetic images, then image synthesis capability is improved, but artifact quality deteriorates
Solution Approach 1:
The patent applies feedback by computing pixel-wise metrics based on gradient magnitudes from the synthetic image and using these metrics to guide iterative refinement. The system automatically identifies artifacts through the metric computation and feeds this information back into the generation process, allowing continuous improvement of image quality until convergence criteria are met.
Solution Approach 2:
The patent changes parameters by iteratively modifying the synthetic image based on computed metrics. When artifacts are detected through gradient magnitude analysis, the system adjusts image parameters (such as replacing pixel values with random noise or adjusting generation parameters) to eliminate artifacts and improve overall image quality.
2Reliability
If automated artifact detection is implemented, then image quality control is improved, but processing complexity increases
Solution Approach 1:
The patent segments the artifact detection process into discrete, manageable steps: computing gradients for each pixel, calculating metric values based on gradient magnitudes, comparing metrics against thresholds, and identifying artifact regions. This segmentation allows complex quality control to be broken down into simple, executable operations that maintain reliability while managing computational complexity.
Solution Approach 2:
The system performs self-service by automatically computing its own quality metrics and using these metrics to guide self-correction. The synthetic image generation system inherently monitors its own output quality through pixel-wise metric computation and automatically initiates refinement processes without external intervention, reducing the need for separate complex quality assessment systems.
Data Source
AI summary
A computer implemented method for digital image processing. The method includes: determining a synthetic digital image with a text to image diffusion depending on an input that represents a digital image, a noise sample, and an embedding that represents the text. The text to image diffusion includes a forward diffusion process to determine a noisy latent depending on the input and the noise sample. The noisy latent is parametrized by parameters. The text to image diffusion includes a backward denoising process to determine an output that represents the synthetic digital image depending on a linear combination of the noisy latent and predicted noise. The synthetic digital image includes pixels. The method includes determining for at least one pixel a magnitude of a gradient with respect to the parameters of a difference between the predicted noise for the pixel and the noise sample for the pixel.