Generative ISP Ensemble Processing for Robust Machine Vision
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Solution Overview
Problem
Existing image signal processors (ISPs) are optimized for human visual perception and are not suitable for machine vision tasks, limiting the performance of machine vision systems in applications such as autonomous vehicles and defect detection.
Innovation Solution
Implementing a generative image signal processor (ISP) that uses generative models like GANs or diffusion models to generate multiple output images with different noise inputs, which are then ensembled to improve machine vision performance by enhancing robustness and adaptability to various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image signal processors are optimized for human visual perception, then human visual quality is improved, but machine vision task performance deteriorates
Solution Approach 1:
The system segments the image processing workflow into two distinct components: a generative model that creates multiple diverse output images from a single input, and an ensemble machine vision model that processes these multiple images separately and combines their results. This segmentation allows each component to be optimized for its specific function, with the ensemble structure specifically tailored for machine vision tasks while the generative model handles the diversity generation.
Solution Approach 2:
The generative model acts as an intermediary between the input image and the machine vision model. It transforms a single input image into multiple diverse output images with different noise patterns and semantic information, which then serve as input to the machine vision model. This intermediary layer enables the system to bridge the gap between human visual perception optimization and machine vision task requirements.
2Reliability
If multiple output images are generated through random sampling with different noise inputs, then robustness and adaptability are improved, but processing complexity increases
Solution Approach 1:
The system applies periodic action by generating multiple output images with different random noise patterns (Gaussian and Poisson sampling) and then systematically ensembling their results. This periodic generation of diverse images with controlled noise variations allows the machine vision model to learn from multiple perspectives while maintaining a structured processing approach that can be managed through algorithmic ensemble methods.
Solution Approach 2:
The generative model changes parameters by introducing different noise patterns (Gaussian and Poisson sampling with varying sigma values) and generating images with different semantic information. This parameter variation in the input images allows the machine vision model to become more robust to different conditions while the systematic nature of the parameter changes keeps the complexity manageable through controlled experimentation.
3Adaptability or versatility
If generative models are used to generate diverse output images, then adaptability to various environments is improved, but computational resources increase
Solution Approach 1:
The generative model performs preliminary action by pre-generating multiple diverse output images with different noise patterns and semantic variations before the machine vision model processes them. This preliminary generation of diverse inputs allows the system to adapt to various environmental conditions in advance, and the ensemble method efficiently combines these pre-generated images to produce the final result, reducing the need for real-time adaptive processing.
Data Source
AI summary
A method and apparatus will machine learning-based image processing is provided. The method includes generating a plurality of output images using a generative model that is provided a raw image of an image sensor, generating, using a machine vision model that is provided the plurality of output images, plural output data respectively corresponding to the plurality of output images, generating result data of the machine vision model by performing an ensemble on the plural output data.