Synthetic Multi-Frame Training Data for ML Image Post-Processing
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Solution Overview
Problem
Generating high-quality training data for machine learning models in computational photography is challenging due to difficulties in simulating image and noise statistics, especially in multi-frame processing, and capturing clean ground truth images is costly and time-consuming.
Innovation Solution
A method involving generating synthetic training data using a ground truth image, modeled optical blur, global motion, and noise models to create realistic input frames, which are then processed through a multi-frame pipeline to train a machine learning-based image processing engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synthetic training data is generated using modeled optical blur, global motion, and noise models, then training efficiency is improved and costly manual data preparation is reduced, but the complexity of the data generation pipeline increases
Solution Approach 1:
The patent applies preliminary action by pre-modeling optical blur, global motion, and noise characteristics before generating training data. These models are prepared in advance and systematically applied to ground truth images to create realistic synthetic training datasets, eliminating the need for costly manual data preparation while maintaining training efficiency
Solution Approach 2:
The patent introduces intermediary components including a color filter array model, noise model, and multi-frame processing pipeline that mediate between ground truth images and final synthetic training data. These intermediaries systematically transform raw images into realistic training samples with controlled degradation, managing pipeline complexity through structured intermediate representations
2Manufacturing precision
If multiple image frames are generated with modeled optical blur and global motion to simulate realistic conditions, then the quality of training data is improved, but the time and computational resources required for data generation increase
Solution Approach 1:
The patent applies copying by generating multiple synthetic image frames from single ground truth images through systematic application of modeled optical blur, global motion, and noise. This creates realistic copies that simulate varied capture conditions without requiring multiple physical captures, improving training data quality while reducing time loss compared to manual data collection
Solution Approach 2:
The patent employs parameter changes by systematically varying optical blur parameters, global motion parameters, and noise levels across multiple generated frames. These controlled parameter variations create diverse training samples from single inputs, enhancing data quality while maintaining efficient generation through computational parameter manipulation rather than physical re-capture
Data Source
AI summary
A method includes obtaining a ground truth image and generating multiple image frames using the ground truth image, a modeled optical blur, and a modeled global motion. The method also includes generating multiple mosaic image frames using the image frames and a color filter array and generating multiple raw input image frames using the mosaic image frames and a noise model associated with at least one imaging sensor. The method further includes providing the raw input image frames to a multi-frame processing pipeline in order to generate synthetic training data. In addition, the method includes training a machine learning-based image processing engine using the ground truth image and the synthetic training data.