Deep Learning Harmonization of Composite Images
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Solution Overview
Problem
Current image harmonization methods often produce unrealistic results when trying to match the color and tone of foreground and background regions in composite images, especially when the regions are significantly different, and they struggle to maintain realism in complex scenes.
Innovation Solution
A neural network system is trained to generate harmonized composite images by learning accurate coloration and tone relationships between foreground and background regions using a dataset of synthesized composite images, where the system adjusts its output based on errors compared to reference images to ensure realistic and uniform color schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If color matching methods are used to harmonize composite images, then color compatibility between foreground and background is improved, but the realism of the result deteriorates when regions are greatly different
Solution Approach 1:
The patent introduces an intermediary representation called 'style code' that mediates between the content image and reference image. Instead of directly matching colors between foreground and background, the style code captures the essential color and tone characteristics of the reference image and applies them as a intermediate layer, allowing for more realistic harmonization while maintaining the semantic content of the original image.
Solution Approach 2:
The patent transforms the image harmonization problem into a parameter optimization problem by learning style codes that represent color and tone characteristics. The system searches for optimal style code parameters that minimize the difference between the harmonized image and the reference image while preserving content integrity, enabling flexible adjustment of color and tone without losing realism.
2Manufacturing precision
If example-based color transfer methods are used, then color scheme consistency is improved, but the method fails when no proper exemplar image can be found
Solution Approach 1:
The patent creates a universal harmonization system that can work with any reference image regardless of its content or style. The style code representation is designed to be content-agnostic, capturing only the color and tone characteristics that can be applied to any foreground-background combination. This makes the method universally applicable across different scenes, objects, and reference images without requiring specific exemplar matching.
3Productivity
If traditional color matching approaches are used, then processing speed is maintained, but the accuracy of coloration and tone learning deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-training the network to extract and represent color and tone characteristics in the form of style codes. This pre-extraction of colorimetric information allows the system to quickly apply harmonization without requiring complex iterative color matching during actual processing, thus maintaining fast processing speed while achieving high coloration and tone accuracy through the learned style code representation.
Data Source
AI summary
Methods and systems are provided for generating harmonized images for input composite images. A neural network system can be trained, where the training includes training a neural network that generates harmonized images for input composite images. This training is performed based on a comparison of a training harmonized image and a reference image, where the reference image is modified to generate a training input composite image used to generate the training harmonized image. In addition, a mask of a region can be input to limit the area of the input image that is to be modified. Such a trained neural network system can be used to input a composite image and mask pair for which the trained system will output a harmonized image.