Neural Image Relighting With Per-Pixel Lighting for Background Replacement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile device users lack access to professional image relighting and compositing features, resulting in inconsistent foreground lighting and blurred object boundaries in images, especially when compositing into different backgrounds.
Innovation Solution
A computing device equipped with a machine-learned system that utilizes a neural network for foreground relighting and background replacement, employing a per-pixel lighting representation to ensure consistent lighting and accurate foreground separation, allowing for real-time enhancement of images on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional compositing techniques are used, then background replacement can be achieved, but foreground lighting becomes inconsistent with background lighting
Solution Approach 1:
The patent applies parameter changes by transforming the foreground image through multiple processing stages including color space conversion, histogram matching, and lighting adjustment. The system changes color distribution parameters and lighting parameters to match the target background's illumination characteristics, thereby achieving consistent lighting while maintaining background replacement capability.
Solution Approach 2:
The system employs feedback mechanisms through iterative optimization processes where the relit foreground is composited with the target background and the result is evaluated. The system adjusts lighting parameters based on the comparison between the original and relit images, using feedback loops to converge on optimal lighting consistency.
2Productivity
If simple image processing is used, then processing speed is fast, but boundary details become blurred
Solution Approach 1:
The patent segments the image processing into distinct modules: foreground extraction, lighting analysis, relighting processing, and compositing. Each module handles specific tasks independently, allowing optimized processing at each stage. The segmentation enables detailed boundary preservation through dedicated edge detection and maintenance operations while maintaining overall processing efficiency.
Solution Approach 2:
The system applies local quality enhancement by differentiating processing intensity across different image regions. Boundary areas receive enhanced processing for detail preservation, while uniform regions undergo standard processing. The patent implements local adaptive algorithms that adjust processing parameters based on region characteristics, ensuring high-frequency detail preservation at object boundaries without compromising overall processing speed.
3Reliability
If professional studio resources are used, then image quality is high, but accessibility is limited to experts
Solution Approach 1:
The patent implements self-service through automated machine learning models that perform relighting and compositing without requiring user expertise. The system automatically analyzes the target background, determines appropriate lighting parameters, and executes the relighting process autonomously. Users simply provide the input image and target background, and the system handles the complex processing independently, making professional-quality results accessible to general users.
Solution Approach 2:
The system introduces machine learning models and automated algorithms as intermediaries between the user and the complex image processing tasks. These intermediaries translate simple user inputs into sophisticated relighting operations, shielding users from the complexity of professional studio equipment and techniques while delivering high-quality results.
4Measurement precision
If complex lighting models are used, then relighting accuracy is high, but computational requirements increase
Solution Approach 1:
The patent applies partial action by implementing relighting models that use a subset of necessary computational operations to achieve sufficient accuracy for mobile applications. The system selectively applies complex lighting calculations only where needed, using simplified models for common lighting conditions and reserving computational resources for edge cases. This partial approach maintains acceptable relighting accuracy while significantly reducing overall computational requirements for mobile devices.
Data Source
AI summary
Apparatus and methods related to applying lighting models to images are provided. An example method includes receiving, via a computing device, an image comprising a subject. The method further includes relighting, via a neural network, a foreground of the image to maintain a consistent lighting of the foreground with a target illumination. The relighting is based on a per-pixel light representation indicative of a surface geometry of the foreground. The light representation includes a specular component, and a diffuse component, of surface reflection. The method additionally includes predicting, via the neural network, an output image comprising the subject in the relit foreground. One or more neural networks can be trained to perform one or more of the aforementioned aspects.