Self-Supervised Image Harmonization Neural Network
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Solution Overview
Problem
Conventional digital image editing systems face challenges in accuracy, efficiency, and flexibility when it comes to image harmonization, requiring users to iteratively adjust brightness, color, contrast, and positioning, often leading to unsatisfactory results.
Innovation Solution
The implementation of a self-supervised image harmonization neural network that automatically matches color tone, brightness, and contrast between digital images by extracting content from one image and appearance from another, using a dual data augmentation method to generate diverse triplets without the need for large-scale training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital image editing systems are used for image harmonization, then users can manually adjust brightness, color, and contrast, but the process requires iterative manual adjustment and leads to unsatisfactory results for non-expert users
Solution Approach 1:
The system performs automatic image harmonization without requiring user intervention. The neural network independently analyzes the foreground and background images, extracts relevant features, and applies harmonization adjustments automatically, eliminating the need for manual iterative adjustment while achieving expert-level results
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated neural network system. The deep learning model substitutes human operators by performing feature extraction, color matching, and harmonization adjustments through computational algorithms rather than manual slider adjustments
2Reliability
If deep learning models are trained using conventional supervised learning approaches, then the models can learn from labeled data, but large-scale training datasets are required which increases data collection and processing requirements
Solution Approach 1:
The system performs self-supervised learning by automatically generating its own training data and labels. The neural network creates synthetic training triplets (foreground image, background image, harmonized result) and uses these self-generated examples to train and refine its harmonization capabilities without requiring external labeled datasets
Solution Approach 2:
The system pre-processes images to generate training data before actual model training. By creating synthetic training examples in advance through automated image processing and harmonization, the system prepares sufficient training material without needing to collect large-scale real-world labeled datasets
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.