Segmented Image Editing to Preserve Human Image Features
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Solution Overview
Problem
Existing image editing techniques, particularly those using diffusion machine learning models, often produce undesirable results due to biased training data, leading to excessive or inappropriate editing of image features, especially in images of humans, where certain characteristics are associated with specific professions.
Innovation Solution
A method that involves image segmentation to identify protected and non-protected areas, applying different parameter sets for each area to control the editing strength, using a combination of latent and pixel information, and a denoising process to generate edited images while preserving specific features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image editing processes are used to improve productivity, then editing speed increases, but excessive or undesirable editing of original image features occurs
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) based on semantic segmentation, allowing different editing strengths to be applied to different areas. This enables automated editing while preserving important features in specific regions by identifying and protecting semantically meaningful areas from excessive modification.
Solution Approach 2:
Different editing parameters and strengths are applied locally to different regions of the image based on their semantic importance. Regions identified as containing important features receive lower editing strength, while less critical areas can be edited more aggressively, achieving both automation and feature preservation.
2Manufacturing precision
If manual pixel-level manipulation is used to preserve image features, then editing precision improves, but work time increases significantly
Solution Approach 1:
A semantic segmentation model serves as an intermediary that automatically identifies important regions, replacing the need for manual pixel-level analysis. This intermediary system provides the precision needed for feature preservation while operating automatically, thus eliminating the time-consuming manual manipulation requirement.
Solution Approach 2:
The system performs automatic semantic segmentation and region identification without requiring manual intervention. The algorithm autonomously analyzes the image, identifies important features, and applies appropriate editing parameters, enabling the system to serve itself rather than requiring human operators for precise feature protection.
3Productivity
If strong editing parameters are applied to achieve desired modifications, then editing effectiveness increases, but original image features are excessively altered
Solution Approach 1:
The editing parameters are made dynamic and adaptive based on the semantic content of each region. Instead of applying uniform strong editing parameters across the entire image, the system adjusts parameter strength dynamically according to the importance of each region, allowing effective editing where needed while preserving original features where critical.
Data Source
AI summary
Described embodiments generally relate to a computer-implemented method for editing an image. The method includes accessing an image; identifying at least a first area of the image and a second area of the image; configuring a model to generate an edited image based on the first area of the image and the second area of the image, wherein the edited image comprises a first area of the edited image and a second area of the edited image; wherein the model is configured to generate the edited image such that the first area of the edited image differs from the first area of the image less than the second area of the edited image differs from the second area of the image.