Camera Noise Profiling Across Brightness and Color Temperature
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Solution Overview
Problem
Existing image processing systems face challenges in generating accurate noise profiles due to varying image noise caused by different camera lens and sensor combinations and ISO values, making it difficult to set optimal noise reduction parameters.
Innovation Solution
A method and system for generating a noise profile by capturing test images under various brightness conditions and color temperatures, computing noise profiles from these images, and adjusting image processing parameters accordingly to reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise reduction function is implemented with fixed parameters, then processing speed is maintained, but noise reduction accuracy deteriorates due to varying image noise from different lens-sensor combinations and ISO values
Solution Approach 1:
The patent performs preliminary actions by capturing test images under multiple predetermined test combinations (different lens-sensor combinations and ISO values) before actual image processing. The noise profile is computed in advance from these test images and stored for later use, eliminating the need for real-time parameter adjustment during actual imaging operations.
Solution Approach 2:
The patent creates a noise profile that copies and characterizes the noise characteristics from various test combinations. This noise profile serves as a template or model that can be applied to different imaging conditions without requiring re-computation, effectively copying the essential noise behavior patterns for efficient noise reduction processing.
2Measurement precision
If multiple test combinations are captured to compute accurate noise profile, then noise profile accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs the time-consuming task of capturing test images and computing noise profiles as a preliminary action during device setup or calibration phase. Once the noise profile is computed and stored, subsequent image processing operations can proceed quickly without re-performing the time-consuming measurements, thus separating the one-time computational cost from the repeated processing operations.
Data Source
AI summary
A noise profile generation method, for generating a noise profile of an image recording device, comprising: (a) capturing a plurality of test images of a test object by using a plurality of test combinations by the image recording device responding to a control command, wherein each of the test combinations comprises a brightness condition and a color temperature; and (b) computing the noise profile according to the test images.