Differentiable ISP for Machine Vision Domain Adaptation
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Solution Overview
Problem
Existing machine vision systems face challenges in optimizing the end-to-end performance due to visual domain shifts caused by changes in sensor configurations, requiring extensive labelled data and separate optimization of image signal processors (ISPs) and perception modules, which is costly and inefficient.
Innovation Solution
A differentiable image signal processor (ISP) is trained using semi-supervised learning to adapt raw images from a new sensor into the same visual domain as the training data, allowing joint optimization with the perception module without the need for labelled data, using a block-wise differentiable architecture with functional modules for specific image processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional ISP is designed to optimize images for human viewers, then image quality for human perception is improved, but the images are not optimized for downstream non-human perception modules
Solution Approach 1:
The patent changes the optimization parameters of the ISP from human visual perception metrics to task-specific perception module requirements. The ISP learns to transform raw images into adapted images with statistical properties matched to the training data domain, rather than optimizing for human viewers. This parameter transformation enables the same ISP to serve both human and machine perception needs.
Solution Approach 2:
The patent introduces dynamic adaptability by training the ISP to learn domain-specific transformations based on the target perception module's requirements. The ISP becomes dynamically adjustable to different perception tasks through learned parameters, allowing it to adapt its image transformation behavior according to the specific downstream application rather than being static for human viewing only.
2Reliability
If the imaging pipeline is optimized for a specific perception task, then task performance is improved, but the system requires extensive labelled data and fine-tuning
Solution Approach 1:
The patent uses domain adaptation through statistical moment matching to create a virtual copy of the target domain distribution. Instead of requiring actual labelled training data from the target sensor, the method synthesizes adapted images that statistically mimic the training data domain characteristics. This copying approach allows the perception module to generalize across different sensors without needing extensive labelled fine-tuning data.
Solution Approach 2:
The patent changes the optimization objective from task-specific loss minimization requiring labelled data to unsupervised domain adaptation using statistical moment matching. By matching higher-order statistical moments between source and target domains, the system achieves domain invariance without requiring labelled training data, thus maintaining high perception task performance while eliminating the need for extensive labelled fine-tuning.
3Ease of manufacture
If separate optimization is performed for ISP and perception module, then each component can be optimized independently, but end-to-end performance optimization is hindered
Solution Approach 1:
The patent merges the ISP optimization with the perception module's domain requirements by introducing a domain adaptation loss that connects the two components. The ISP is trained jointly with the perception module using a combined objective that includes both image quality metrics and domain matching metrics. This merging allows gradients to flow end-to-end, enabling simultaneous optimization of both the ISP and perception module for maximum end-to-end performance while maintaining independent component design.
Data Source
AI summary
End to end differentiable machine vision systems, training methods, and processor-readable media are disclosed. A differentiable image signal processor (ISP) is disclosed that can be trained, using machine learning techniques, to adapt raw images received from a new sensor into an adapted images of the same type (i.e. in the same visual domain) as the images previously used to train a perception module, without fine-tuning the perception module itself. The differentiable ISP may include functional blocks for performing specific image enhancement operations using a relatively small number of learned parameters corresponding to meaningful characteristics of the image enhancement operations.