Two-Stream Encoder Neural Network for Natural Image Composition
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Solution Overview
Problem
Conventional image composition systems face inaccuracies and inflexibility in generating composite digital images, often resulting in unnatural artifacts and requiring significant manual user input, while also struggling with limited adaptability to training data.
Innovation Solution
A multi-level fusion neural network with a two-stream encoder architecture is employed to extract multi-scale features from foreground and background images, using a decoder for natural blending and an easy-to-hard data augmentation scheme for self-teaching and end-to-end automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional cut-and-paste methods are used to extract and combine foreground objects, then the process is simple and fast, but unnatural artifacts appear along the boundary of the foreground object
Solution Approach 1:
The patent introduces an intermediary blending process between the foreground and background images. Instead of direct cut-and-paste, the system applies blending algorithms (such as Poisson blending, Laplacian pyramid blending, or guided filtering) that act as mediators to smoothly transition pixels at the boundary, eliminating the harsh linear combination artifacts while maintaining computational efficiency.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. High-quality blending algorithms are applied specifically at the boundary regions where artifacts occur, while the interior regions of the foreground object and the background maintain their original quality. This localized approach improves boundary realism without unnecessarily processing the entire image at high computational cost.
2Manufacturing precision
If low-level image blending methods are applied to reduce boundary artifacts, then boundary realism improves, but color distortion and non-smooth halo artifacts are introduced
Solution Approach 1:
The patent employs feedback mechanisms where the blending process is iteratively refined. The system monitors the output for color distortion and halo artifacts, then adjusts blending parameters or applies correction passes to eliminate these harmful effects. This feedback loop ensures that while boundary realism is improved, the introduction of new artifacts is minimized or corrected.
Solution Approach 2:
The patent combines multiple blending methods and correction techniques into a composite processing pipeline. Rather than relying on a single blending algorithm that may introduce artifacts, the system layers multiple processing steps (different blending algorithms, color correction, halo removal) that work together to achieve boundary realism while compensating for the weaknesses of individual methods.
3Manufacturing precision
If image matting methods are used to combat boundary artifacts, then boundary accuracy improves, but significant manual user input is required
Solution Approach 1:
The patent implements self-service automation where the system automatically performs segmentation, matting, and blending operations without requiring manual user input. The system uses automated algorithms to identify foreground objects, generate segmentation masks, and apply appropriate blending techniques. This automation maintains high boundary accuracy while eliminating the need for users to provide trimaps or manually guide the process.
Solution Approach 2:
The patent creates a universal image composition system that handles multiple tasks (segmentation, matting, blending, color matching) through a single integrated framework. This multi-functional system can process various types of images and boundary conditions using the same automated pipeline, providing high boundary accuracy across different scenarios without requiring task-specific manual intervention.
4Reliability
If conventional systems are designed for specific image composition tasks, then task performance is optimized, but adaptability to different conditions and limited training data is reduced
Solution Approach 1:
The patent implements dynamic adaptability where the system can adjust its processing parameters, blending strategies, and model configurations based on the specific characteristics of the input images and available training data. Rather than being fixed for a single task, the system dynamically selects and tunes algorithms to match the current composition requirements, maintaining reliability across diverse tasks while adapting to limited training data through transfer learning and data augmentation.
Data Source
AI summary
The present disclosure relates to utilizing a neural network having a two-stream encoder architecture to accurately generate composite digital images that realistically portray a foreground object from one digital image against a scene from another digital image. For example, the disclosed systems can utilize a foreground encoder of the neural network to identify features from a foreground image and further utilize a background encoder to identify features from a background image. The disclosed systems can then utilize a decoder to fuse the features together and generate a composite digital image. The disclosed systems can train the neural network utilizing an easy-to-hard data augmentation scheme implemented via self-teaching. The disclosed systems can further incorporate the neural network within an end-to-end framework for automation of the image composition process.