sCMOS Camera Noise Filtering via Sparse 3D Transform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Scientific CMOS cameras face challenges in minimizing noise, particularly fixed-pattern noise and readout noise, which degrade image quality, especially in low-light conditions, due to inherent characteristics of the camera design and readout techniques.
Innovation Solution
A method involving camera calibration, noise estimation, and sparse filtering is employed to remove fixed-pattern noise and readout noise from image data. This includes loading camera parameters, calculating an optical transfer function, generating a high-pass filter, and applying sparse filtering techniques such as three-dimensional transforms and Wiener filtering to isolate and remove noise while preserving image details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sCMOS cameras are used for rapid readout, then frame rate and readout speed are improved, but fixed-pattern noise and pixel variability increase
Solution Approach 1:
The patent applies preliminary action by performing camera calibration before image acquisition to determine fixed-pattern noise parameters. The calibration process characterizes pixel-specific offsets, variances, and gains in advance, creating lookup tables and statistical models that are then used during actual imaging to correct the captured frames, thereby reducing fixed-pattern noise while maintaining rapid readout speeds
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting correction parameters based on the specific imaging conditions and camera settings. The system modifies gain values, offset corrections, and variance parameters according to the actual photon counts and exposure conditions, allowing optimal noise correction across varying operational parameters while preserving the high-speed readout capability
2Measurement precision
If noise correction methods are applied, then image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent reduces computational complexity during image processing by performing all complex calibration and parameter estimation operations in advance. The lookup tables containing pixel-specific correction parameters are pre-computed during calibration, allowing the actual image correction to use simple table lookups and basic arithmetic operations rather than complex real-time calculations
Solution Approach 2:
The patent uses copying by creating and storing lookup tables that replicate the complex correction relationships. Instead of performing complex calculations on each image frame, the system copies pre-computed correction parameters into lookup tables that can be quickly referenced during image processing, significantly reducing the computational burden while maintaining correction accuracy
3Ease of operation
If classic white noise assumptions are used, then processing simplicity is maintained, but noise estimation accuracy deteriorates at low photon counts
Solution Approach 1:
The patent applies local quality by using pixel-specific noise models that account for the unique characteristics of each pixel rather than applying a uniform white noise assumption. The system determines individual pixel offsets, variances, and gains during calibration, allowing the noise correction to be tailored to the actual noise properties of each pixel, which is particularly important for accurate correction at low photon counts where noise characteristics vary significantly across the sensor
Data Source
AI summary
In a method of filtering an image from data received from a CMOS camera, image data is loaded by a computational device from the camera. Camera parameters corresponding to the CMOS camera are loaded. Fixed pattern noise associated with the camera is removed based on the camera parameters. A readout noise estimation based on characteristics of the camera and filtering estimated readout noise from the image data is generated. Sparse filtering: selecting sub-frames within the image that have similar features; applying a three-dimensional transform on the sub-frames transforming the sub-frame data into a non-two-dimensional domain and generating a first transformed data set; filtering noise data from the first transformed data set to generate a first thresholded image data set; and applying a reverse three-dimensional transform on the first thresholded image data set so as to generate an image.