Image Processing With Linked Noise Models for Statistical Consistency
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Solution Overview
Problem
Existing image processing methods fail to maintain the statistical properties of raw image data due to signal-dependent noise, leading to inconsistent noise across the image, which affects the reliability of processing algorithms, especially in machine learning applications.
Innovation Solution
A method that integrates an input noise model with raw image data, allowing for processing operations that maintain a consistent noise model throughout the image processing pipeline, including corrections for imperfections and synthesis of synthetic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear corrections such as scaling of pixel values or substituting pixel values by averages of a neighborhood are applied, then the appearance of individual images is improved, but the noise becomes inconsistent across the image
Solution Approach 1:
The patent applies non-linear correction functions that adaptively adjust pixel values based on local characteristics and noise models, rather than using fixed linear scaling. This allows the correction to preserve noise consistency while improving image appearance by accounting for signal-dependent noise behavior in each region of the image.
Solution Approach 2:
The patent incorporates noise models that provide feedback about the statistical properties of the image data into the correction process. By continuously monitoring and adjusting corrections based on the noise characteristics, the system maintains noise consistency across the image while still achieving improved appearance.
2Measurement precision
If bad pixel replacement methods using averages or statistical functions are applied, then corrupted pixels are corrected, but the noise level becomes below expectation
Solution Approach 1:
Instead of using fixed average or statistical functions for bad pixel replacement, the patent employs non-linear correction functions that adapt to the local noise characteristics and signal levels. This ensures that replaced pixels maintain the expected noise level consistent with the surrounding areas while still achieving accurate correction.
3Manufacturing precision
If flat-field correction using normalization to pixel-specific response curves is applied, then non-uniform sensor response is corrected, but the statistical characteristics of the image data are worsened
Solution Approach 1:
The patent uses non-linear correction functions that adaptively adjust pixel values based on local characteristics and noise models, rather than applying fixed linear normalization. This allows the correction to preserve noise consistency and statistical characteristics while still achieving uniform sensor response across the image.
Data Source
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AI summary
The present invention concerns a method for processing of image data, wherein said image data comprises noise and information, the method comprising the steps of - acquiring input data (100) comprising input raw image data (110) to be processed for storage and/or transmission, said input raw image data (110) comprising values y of pixels of an image sensor used to take the image data, - processing (130) said input raw image data (110), - outputting the processed image data by providing output data (140). The method distiguishes by the fact that the step of acquiring input data (100) comprises acquiring an input noise model (120) from said input data (100), by the fact that the step of processing (130) said input raw image data (110) comprises at least one preprocessing or image synthesis operation and further comprises determining an output noise model (160) adapted to reflect noise present in said output data (140) as well as producing output raw image data (150) which is statistically consistent with said output noise model (160), and by the fact that the step of outputting the processed image data comprises storing and/or transmitting said output raw image data (150) and said output noise model (160), which together form said output data (140), in a manner linking the output raw image data (150) to the output noise model (160), such as to allow for processing (130) of said output data (140), as input data (100), by any one of said processing operations, alone or in any combination thereof, such that said processing (130) is adapted for pipeline processing.