Bayesian Image Denoising via Distribution Constraint
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image denoising methods, including classical filtering and deep learning techniques, face challenges in preserving image details and require large datasets of noiseless images for training, which can be difficult and expensive to obtain.
Innovation Solution
A Bayesian image denoising method that determines a distribution constraint of noiseless images from noisy images using characteristic functions and neural network discriminators, allowing for unsupervised learning and efficient denoising without relying on paired noiseless data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for image denoising, then image details can be recovered more accurately, but large numbers of training datasets are required which are difficult and expensive to obtain
Solution Approach 1:
The patent introduces a neural network discriminator as an intermediary component that enables the system to learn from noisy images alone. The discriminator acts as a mediator between the generator network and the noisy image data, allowing the system to infer noiseless image distributions without requiring direct access to ground truth noiseless images for training.
Solution Approach 2:
The patent creates a synthetic copy of the noiseless image distribution by training the generator network to produce images that match the statistical properties learned from noisy images. The neural network discriminator validates this copied distribution, enabling the system to work with synthesized data rather than requiring authentic noiseless training pairs.
2Object-generated harmful factors
If classical filtering methods are used for image denoising, then noise removal can be achieved, but image details are not preserved well
Solution Approach 1:
The patent transforms the denoising problem from direct spatial domain filtering to a distribution-based approach in the frequency domain. By changing the parameter space from pixel values to characteristic function parameters, the system can remove noise while preserving the statistical properties that encode image details.
Solution Approach 2:
The patent replaces traditional mechanical filtering operations with a neural network-based system that learns optimal denoising strategies. Instead of applying fixed mathematical filters, the system uses trained neural networks that can adaptively remove noise while preserving details based on learned patterns from the data distribution.
Data Source
AI summary
Embodiments of the present disclosure provide a Bayesian image denoising method based on a distribution constraint of noiseless images from a distribution of noisy images. The method includes: determining a distribution constraint of noiseless images from a distribution of noisy images; and performing Bayesian denoising on noisy images based on the distribution constraint of noiseless images.