Artifact Segmentation for Iterative Image Inpainting Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image generation systems introduce perceptual artifacts such as broken structures, color blobs, and color bleeding into synthetically generated digital images, lacking accuracy and flexibility in digital image inpainting and editing, especially for large holes or complex structures, and require manual user corrections.
Innovation Solution
An artifact segmentation system utilizing a machine-learning model trained on user-labeled perceptual artifacts to detect and iteratively inpaint digital images, reducing perceptual artifacts by selecting from multiple inpainting models based on artifact ratio metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image generation systems are used to generate or modify digital images, then the processing speed is maintained, but perceptual artifacts such as broken structures, color blobs, and color bleeding are introduced into the synthetically generated portions
Solution Approach 1:
The system segments the digital image into different regions: original regions and synthetically modified regions. By applying the artifact segmentation model specifically to the synthetically modified portions, the system can detect and address perceptual artifacts without reprocessing the entire image, thus improving image quality while maintaining efficiency.
Solution Approach 2:
The artifact segmentation model acts as an intermediary between the image generation model and the final output. It detects perceptual artifacts in the synthetically generated portions and generates artifact segmentation masks that guide subsequent refinement processes, thereby reducing artifacts while preserving the benefits of synthetic generation.
2Reliability
If manual user corrections are performed to fix perceptual artifacts, then image quality improves, but the ease of operation decreases and processing time increases
Solution Approach 1:
The system implements self-service by automatically detecting perceptual artifacts using the artifact segmentation model and generating artifact segmentation masks without requiring manual user intervention. The iterative inpainting process automatically refines the synthetically modified portions based on detected artifacts, eliminating the need for manual corrections while maintaining high image quality.
Solution Approach 2:
The system employs feedback mechanisms where the artifact segmentation model continuously evaluates the synthetically generated portions and provides artifact segmentation masks back to the inpainting process. This closed-loop feedback enables automatic detection and correction of perceptual artifacts, improving image quality without manual intervention.
3Device complexity
If a single inpainting model is used for all scenarios, then the device complexity is reduced, but the adaptability to different types of digital image content decreases
Solution Approach 1:
The system dynamically selects from multiple inpainting models based on the characteristics of the digital image content and the detected perceptual artifacts. The artifact segmentation model identifies specific artifact types and patterns, which then guide the selection of the most appropriate inpainting model for that particular scenario, enabling adaptive processing without requiring a fixed complex model structure.
Solution Approach 2:
The system changes parameters such as the selected inpainting model, iteration count, and processing resolution based on the analysis of the digital image content and artifact characteristics. This parameter adaptation allows the system to optimize performance for different types of content (e.g., photographs, illustrations, medical images) without requiring fundamentally different system architectures.
4Manufacturing precision
If multiple iterations of inpainting are performed to reduce perceptual artifacts, then the manufacturing precision of the image quality improves, but the productivity decreases
Solution Approach 1:
The system applies partial action by performing inpainting iterations only on the regions identified as containing perceptual artifacts, rather than reprocessing the entire image. The artifact segmentation masks enable the system to focus computational resources on specific problem areas, achieving high inpainting accuracy while minimizing overall processing time and improving productivity.
Solution Approach 2:
By segmenting the image into artifact-containing regions and non-artifact regions, the system can apply multiple inpainting iterations selectively only to the artifact regions. This segmented approach maintains high manufacturing precision in critical areas while reducing the total computational load and processing time compared to full-image reprocessing.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.