Raw Sensor Image Color Transformation Across Arbitrary Illuminations
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Solution Overview
Problem
Capturing raw sensor images under various illuminations is challenging, requiring significant effort and time, especially for training neural networks, and existing color transformation methods are inaccurate for converting images between arbitrary illuminations.
Innovation Solution
An apparatus and method for converting colors of an input sensor image using a two-step color transform process, involving a first transform to a reference illumination (e.g., daylight) and then to a target illumination, facilitated by neural networks trained on an illumination dataset, allowing for accurate color mapping between arbitrary input and target illuminations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing color transformation methods are used to convert images between arbitrary illuminations, then the conversion process is simple, but the color mapping accuracy is poor
Solution Approach 1:
The color transformation process is divided into two separate transforms: first from input illumination to reference illumination, then from reference illumination to target illumination. This segmentation allows each transform to be optimized independently, improving overall color mapping accuracy while maintaining manageable complexity through modular neural network implementation.
Solution Approach 2:
A reference illumination (e.g., daylight) is introduced as an intermediary between the input illumination and target illumination. This intermediary serves as a common reference point that enables accurate color transformation between arbitrary illuminations by providing a standardized intermediate state for the two-step transform process.
2Reliability
If training datasets are recaptured with new image sensors to account for different characteristics, then the training data quality is high, but the time and effort required is significant
Solution Approach 1:
Instead of physically recapturing training datasets with new image sensors, the method creates synthetic training images by applying color transforms to existing raw sensor images. This copying approach generates high-quality training data that accounts for new sensor characteristics without requiring time-consuming physical recapture under various illuminations.
Solution Approach 2:
The method transforms existing training images by changing their illumination parameters through the two-step color transform process. By modifying the illumination characteristics of existing images to match new sensor profiles, the system generates adapted training data without physical recapture, maintaining reliability while eliminating time loss.
3Adaptability or versatility
If raw sensor images are captured under various illuminations to build comprehensive datasets, then the dataset diversity is high, but the effort and time required is considerable
Solution Approach 1:
A comprehensive illumination dataset is captured once in advance under various illuminations. This preliminary action creates a reusable reference library that enables subsequent color transformations without requiring additional physical captures, thereby achieving high dataset diversity while improving long-term productivity through one-time investment.
Solution Approach 2:
The pre-captured illumination dataset serves multiple functions: it acts as reference data for color transforms, provides training data for neural networks, and enables generation of synthetic images under arbitrary illuminations. This multi-functionality maximizes the utility of the initial data capture effort, achieving versatility without repeated captures.
Data Source
AI summary
A method for processing image data may include: obtaining a raw input image that is captured under an input illumination; obtaining a target illumination from a user input; obtaining an intermediate image having colors captured under a reference illumination, from the raw input image, based on a first color transform that maps the input illumination to the reference illumination in an illumination dataset of raw sensor images that are captured under a plurality of different illuminations; and obtaining an output image having colors captured under the target illumination, from the intermediate image, based on a second color transform that maps the reference illumination in the illumination dataset to the target illumination.