Canonical Space ISP for Cross-Sensor Image Processing
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Solution Overview
Problem
Existing image signal processor (ISP) networks face inefficiencies due to the need for retraining when new sensors are introduced, as they are typically trained on a single domain and require extensive data collection and tuning, leading to inefficiencies in a rapidly changing camera market.
Innovation Solution
A 2-step ISP network approach that includes a canonical image generative model and an image effect augmentation model, allowing for image processing across different sensors without full retraining, by converting images into a canonical color and illumination space and applying style-specific enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ISP networks are trained on a single domain with specific sensor characteristics, then they achieve high processing precision for that sensor, but they require extensive retraining and data collection when new sensors are introduced
Solution Approach 1:
The patent introduces a canonical space as an intermediary representation between different sensor domains. Images from various sensors are first converted to this sensor-agnostic canonical space, which serves as a universal intermediate format. This canonical space acts as the mediator that enables consistent processing across different sensor types without requiring retraining, thus resolving the contradiction between maintaining high precision for specific sensors and achieving versatility across multiple sensor domains
Solution Approach 2:
The image processing pipeline is segmented into distinct stages: conversion to canonical space, processing in canonical space, and conversion back to sensor-specific space. This segmentation allows the core processing logic to operate on the universal canonical representation while handling sensor-specific characteristics separately at the conversion stages, enabling both precision and adaptability
2Measurement precision
If ISP networks are retrained for each new sensor, then processing precision is maintained, but development time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary conversion of images to the canonical space before processing. By pre-converting images to this universal representation, the system eliminates the need for retraining when new sensors are introduced. The canonical space conversion serves as a preliminary action that standardizes input data, allowing the same processing model to be applied across different sensor types without time-consuming retraining cycles
Solution Approach 2:
The patent changes the parameter space by introducing a canonical color space and canonical illumination space as standardized reference frames. Instead of adapting the processing model to different sensor parameters, the approach transforms all sensor-specific images into the standardized canonical parameter space, where a single trained model can process images from any sensor type with maintained precision and without additional retraining time
3Reliability
If extensive data collection is performed for each sensor type, then model performance is optimized, but data collection costs and complexity increase
Solution Approach 1:
The canonical space serves as a universal representation that can handle images from any sensor type. By converting all sensor-specific images to this universal canonical format, the system achieves multi-functionality where a single processing model can reliably process images from diverse sensors. This universality eliminates the need for separate data collection campaigns for each sensor type, reducing data collection complexity while maintaining model performance through consistent canonical space processing
Data Source
AI summary
Provided are an electronic devices and methods for processing an image through a canonical space, the method including receiving training images of a scene obtained from a first sensor, generating first conversion images by converting the training images into images corresponding to a scene obtained through a second sensor, generating second conversion images by converting a color value of the training images and a color value of the first conversion images, and training a canonical image generative model configured to convert the training images, the first conversion images, and the second conversion images into canonical space images of a canonical color space and a canonical illumination space.