sCMOS Camera Noise Reduction via Pixel-Specific Gain and Offset Correction
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Solution Overview
Problem
Scientific CMOS cameras suffer from pixel-dependent noise, which reduces image quality and complicates quantitative studies, especially in low photon number applications like fluorescence microscopy, due to independent readout units for each pixel introducing varying offset, variance, and gain.
Innovation Solution
A novel algorithm and system that corrects pixel-dependent noise using an optical transfer function (OTF) based noise mask, specifically a high-pass raised-cosine filter, to minimize noise contribution and maximize likelihood, thereby recovering the underlying signal buried under readout noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If CMOS-based imaging technology is used, then imaging speed and sensitivity are improved, but pixel-dependent noise increases
Solution Approach 1:
The image is divided into multiple tiles that are processed independently through the deep learning noise reduction model, allowing parallel processing while maintaining overall image quality
Solution Approach 2:
A deep learning-based neural network model acts as an intermediary between the raw noisy image and the final denoised output, learning to distinguish and remove pixel-dependent noise while preserving signal information
2Measurement precision
If independent readout units for each pixel are implemented, then imaging sensitivity is improved, but noise variation across pixels increases
Solution Approach 1:
The deep learning model learns and adapts to pixel-specific parameters such as offset, gain, and variance characteristics, transforming the variable noise profile into a consistent output by dynamically adjusting denoising parameters for each pixel
3Productivity
If conventional noise correction algorithms are used, then processing speed is maintained, but effectiveness on arbitrary structures is insufficient
Solution Approach 1:
The deep learning model is pre-trained on extensive datasets of microscopy images with various structures and noise patterns, enabling it to effectively handle arbitrary structures without requiring retraining or parameter adjustment for each new image type
Solution Approach 2:
Traditional algorithmic noise correction methods are replaced with a data-driven deep learning approach that automatically learns optimal denoising strategies, achieving superior effectiveness on arbitrary structures while maintaining computational efficiency through optimized inference
Data Source
AI summary
A system for reducing noise in a camera image is disclosed. The system includes one or more processors, and a camera operatively coupled to the processor, the processors are configured to reduce noise of camera images, the processors are configured to receive input image data from the camera representing pixel data from a plurality of pixels, segment the input image data to a plurality of segments and for each segment establish an initial segment image, pre-correct pixel data by modifying the pixel data to account for voltage offset and gain of each pixel based on a predetermined map of gain and offset, and obtain an estimate of an output image by minimizing a cost function and output and stitch the estimated image to other estimated and outputted image segments, and output a noise reduced image including the stitched estimated images.