Automated Image Harmonization via Foreground-Background Segmentation
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Solution Overview
Problem
Existing methods for harmonizing a set of digital images, such as those in a document, are time-consuming and impractical for achieving high document quality, as they require manual touch-up operations to make images with different lighting conditions and backgrounds appear homogeneous.
Innovation Solution
An automated image harmonization system that segments each image into foreground and background regions, harmonizes these regions separately, and blends them to create a unified aesthetic, using categorization and target image selection to adjust characteristics like composition, color, and lighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual touch-up operations are used to harmonize images, then image quality and homogeneity are improved, but time consumption and practicality deteriorate
Solution Approach 1:
The system automatically analyzes image characteristics, selects appropriate harmonization parameters, and applies adjustments without requiring manual intervention. The computer executes algorithms that autonomously evaluate lighting conditions, color balances, and composition elements to harmonize multiple images in a set, replacing the need for manual touch-up operations while maintaining high image quality
Solution Approach 2:
The system modifies multiple image parameters simultaneously including brightness, contrast, saturation, temperature, and tint based on automated analysis of the image set. By adjusting these parameters through computational algorithms, the system achieves visual homogeneity across multiple images captured under different conditions, resolving the contradiction between quality improvement and time efficiency
2Manufacturing precision
If multiple image characteristics are adjusted to achieve harmonization, then aesthetic quality is improved, but processing complexity increases
Solution Approach 1:
The system divides the harmonization process into distinct operational modules: image characteristic analysis, parameter selection, and adjustment application. Each module handles specific aspects of the harmonization task, allowing the complex process of adjusting multiple image characteristics to be managed through structured, separate operations that reduce overall processing complexity while maintaining aesthetic quality
Solution Approach 2:
The system employs a unified harmonization framework that simultaneously handles multiple image characteristics including lighting, color, composition, and texture. This multi-functional approach allows a single processing system to adjust various parameters across different images in a coordinated manner, achieving high aesthetic quality without proportionally increasing processing complexity through standardized universal operations
3Stability of the object's composition
If images are processed to match different lighting conditions and backgrounds, then visual consistency is improved, but automation difficulty increases
Solution Approach 1:
The system incorporates automated feedback mechanisms that continuously evaluate image characteristics during processing. By analyzing lighting conditions, background elements, and overall composition, the system adjusts parameters in real-time to achieve visual consistency across images with varying conditions. This feedback-driven approach enables effective automation despite the complexity of matching different environmental conditions
Data Source
AI summary
A harmonization system and method are disclosed which allow harmonization of a set of digital images. The images are automatically segmented into foreground and background regions and the foreground and background regions are separately harmonized. This allows region-appropriate harmonization techniques to be applied. The segmenting and harmonizing may be category dependent, allowing object-specific techniques to be applied.