Image Harmonization for Text Legibility
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Solution Overview
Problem
Graphic designers face challenges in creating harmonious compositions of text and images, as existing methods often require destructive changes to the text, such as adding solid backgrounds, which obscure the underlying image and are not compatible with all image perspectives or color temperatures, and fail to maintain the design features of the text.
Innovation Solution
An image editing system using a generative machine learning model, like a stable diffusion model, processes the image to introduce contrasting colors in the background under the text, preserving the text's design features by applying pre-processing techniques like panoptic segmentation and Gaussian noise, ensuring improved legibility and compatibility with various image conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If solid backgrounds are added to text, then text legibility is improved, but the underlying image is obscured and design features are lost
Solution Approach 1:
Instead of modifying the text by adding solid backgrounds, the invention inverts the approach by modifying the background image itself. The system changes the background colors and properties in the regions where text will be placed, thereby improving text legibility without obscuring or destroying the original image content. This resolves the contradiction by working on the background rather than the text.
Solution Approach 2:
The invention applies local quality by selectively modifying only the background regions that will be covered by text, while preserving the rest of the image. The system identifies text placement areas and applies color changes, brightness adjustments, and other modifications locally to those specific regions, ensuring that text legibility is improved without losing information in the non-text areas of the image.
2Measurement precision
If text effects are applied to improve contrast, then text legibility is improved, but compatibility with various image perspectives and color temperatures is reduced
Solution Approach 1:
The invention employs parameter changes by dynamically adjusting background parameters such as color, brightness, and saturation based on the specific image conditions. The system analyzes the image's color temperature, perspective, and overall characteristics, then modifies the background parameters accordingly to ensure text legibility across diverse image types. This approach maintains compatibility with various image perspectives and color temperatures while improving text legibility.
3Ease of manufacture
If manual image editing techniques are used, then design control is improved, but processing time and complexity increase
Solution Approach 1:
The invention implements self-service by enabling the system to automatically analyze images, determine appropriate text placement regions, and apply suitable background modifications without requiring manual intervention. The AI-powered system autonomously adjusts background parameters, selects color schemes, and optimizes text legibility, thereby maintaining design control while dramatically reducing processing time and complexity compared to manual editing techniques.
Data Source
AI summary
Systems and methods are provided for image editing, and more particularly, for harmonizing background images with text. Embodiments of the present disclosure obtain an image including text and a region overlapping the text. In some aspects, the text includes a first color. Embodiments then select a second color that contrasts with the first color, and generate a modified image including the text and a modified region using a machine learning model that takes the image and the second color as input. The modified image is generated conditionally, so as to include the second color in a region corresponding to the text.