Convolutional Neural Network Training Using Statistical Noise Models
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Solution Overview
Problem
Existing methods for image denoising struggle to effectively remove noise while preserving image features, particularly in low exposure time or gain images contaminated with additive noise.
Innovation Solution
A computer-implemented method for training a convolutional neural network (CNN) that involves generating a statistical noise model based on a captured image and using this model to train the CNN, allowing it to learn noise reduction without relying on traditional training images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If exposure time or gain is reduced to minimize photo bleaching and photo toxicity, then energy input is minimized, but image quality deteriorates due to increased noise
Solution Approach 1:
The patent converts the harmful noise present in low-exposure images into useful training data. By treating noisy images as training samples for a convolutional neural network, the previously harmful noise becomes the basis for teaching the system to recognize and remove noise patterns, thereby transforming the disadvantage of low signal-to-noise ratio into a benefit for training robust denoising models
Solution Approach 2:
The patent creates synthetic training pairs by applying the same noise model to both original and denoised images. Multiple copies of training data are generated through data augmentation techniques including random patches, swapping pixel values with neighbor pixels, and creating probability distributions based on statistical noise models, allowing the network to learn from diverse variations without requiring additional physical images
2Adaptability or versatility
If traditional training images are used to train the CNN, then training data availability is limited, but the method cannot adapt to real acquired images with different noise characteristics
Solution Approach 1:
The system performs self-training by using its own output as training data. The convolutional neural network generates denoised images, which are then used as ground truth to train subsequent iterations of the network. This self-service mechanism allows continuous adaptation to real acquired images without requiring external training datasets, enabling the system to improve its performance autonomously on the specific images it processes
Solution Approach 2:
The patent performs preliminary statistical analysis of the input image to characterize noise properties before training begins. By pre-computing variance or standard deviation values and generating a statistical noise model from the captured image, the system prepares adaptive training data that reflects the actual noise characteristics of the real images, enabling the network to be pre-adapted to the specific imaging conditions before full training commences
Data Source
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AI summary
A computer implemented method for training a convolutional neural network is described. The method comprising receiving a captured image; generating a statistical noise model based on the captured image; and training a convolutional neural network based on the captured image and the statistical model.