Modular Neural Network for High-Resolution Image Denoising
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Solution Overview
Problem
High-resolution imaging techniques, such as FIB-SEM, produce images with significant noise due to optical properties, beam variations, and thermal effects, making it challenging to obtain noise-free images, especially at nanometer scales.
Innovation Solution
A modular machine learning model is trained using sequences of noisy images to identify and remove noise, even in the absence of ground truth images, by comparing different pairs of images in the sequence and using neural networks with branched architectures and building units to iteratively denoise images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging techniques are used, then image resolution is improved, but noise in the image increases
Solution Approach 1:
The patent divides the image denoising task into multiple sequential building units, each responsible for removing specific types of noise. The noisy image is processed through a series of modules (e.g., Gaussian noise removal, Poisson noise removal, impulse noise removal) that progressively clean different noise components, allowing high-resolution imaging to maintain quality while reducing noise through structured multi-stage processing
Solution Approach 2:
The patent employs neural networks with learnable parameters that are trained to optimize noise removal at different resolution scales. The model adapts its parameters based on the input image characteristics, enabling it to effectively reduce noise while preserving the high-resolution details that would otherwise be degraded by traditional denoising methods
2Object-affected harmful factors
If traditional denoising methods are used, then noise is reduced, but image quality and detail are lost
Solution Approach 1:
The patent applies different denoising strategies to different regions and noise types within the image. Each building unit is specialized for removing specific noise patterns (Gaussian, Poisson, impulse) and operates locally on relevant portions of the image, preserving fine details and structures that would be lost in global denoising approaches. The model adapts its processing based on local image characteristics
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic denoising filters with a neural network-based system that learns optimal denoising parameters from training data. This substitution allows the system to intelligently distinguish between noise and meaningful image features, preserving quality while removing noise through learned patterns rather than fixed mathematical operations
3Measurement precision
If ground truth images are required for training, then model accuracy is improved, but training complexity and data requirements increase
Solution Approach 1:
The patent implements a self-supervised training approach where the model learns to denoise images without requiring ground truth annotations. The training process uses synthetic noisy images generated by adding known noise patterns to clean images, allowing the model to learn denoising capabilities through self-supervised learning. This eliminates the need for manually annotated ground truth pairs while maintaining training effectiveness
Solution Approach 2:
The patent performs preliminary preparation of training data by generating synthetic noisy-image pairs through controlled addition of noise patterns to clean images. This preliminary action creates a large dataset of training examples without requiring manual annotation, simplifying the training process while providing sufficient complexity for the model to learn effective denoising patterns
Data Source
AI summary
A machine learning model may be trained to denoise an image. The machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. The machine learning model may include a recurrent neural network. The machine learning model may have a modular architecture including one or more building units. The machine learning model may have a multi-branch architecture. The noise may be identified and removed from the image by an iterative process.