Blind Image Denoising Using Latent Noise Variable Inference
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Solution Overview
Problem
Conventional CNN-based image denoising methods are primarily non-blind, requiring separate models trained for specific noise levels and suffer from performance degradation due to domain discrepancy when faced with different noise levels, limiting their applicability and reliability.
Innovation Solution
An image processing device utilizing a flexible convolutional neural network that infers latent variables from input noisy images, removing noise through a denoising convolutional neural network without additional noise information, and reconstructing images using an encoder and decoder, effectively handling various noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are trained for various noise levels, then noise removal performance is improved for specific noise levels, but device complexity increases and memory requirements increase
Solution Approach 1:
The patent implements a single universal denoising model that can handle multiple noise levels through latent variable inference. The encoder infers latent variables representing noise characteristics from the input image, and the decoder uses these latent variables to perform denoising. This universal model replaces multiple separate models, achieving the ability to handle various noise levels while reducing device complexity and memory requirements.
2Measurement precision
If separate models are trained for various noise levels, then noise removal performance is improved for specific noise levels, but memory requirements increase
Solution Approach 1:
The patent implements a single universal denoising model that can handle multiple noise levels through latent variable inference. The encoder infers latent variables representing noise characteristics from the input image, and the decoder uses these latent variables to perform denoising. This universal model replaces multiple separate models, achieving the ability to handle various noise levels while reducing device complexity and memory requirements.
3Device complexity
If a single neural network is used for various noise levels, then device complexity is reduced and memory requirements are reduced, but noise removal performance may degrade due to domain discrepancy
Solution Approach 1:
The patent introduces latent variables as an intermediary between the input noisy image and the denoising process. The encoder infers these latent variables that capture noise characteristics, and the decoder uses them to adapt the denoising process. This intermediary mechanism enables a single model to effectively handle various noise levels by learning to infer appropriate noise characteristics from the input image, thereby maintaining high noise removal performance while using a single universal model.
4Measurement precision
If additional noise information is provided, then noise removal performance is improved, but ease of operation decreases and device complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the model automatically infers noise characteristics from the input image itself without requiring additional noise information as input. The encoder extracts latent variables that represent noise characteristics directly from the noisy image, and the decoder uses these inferred latent variables to perform denoising. This eliminates the need for users to provide additional noise information, maintaining operational simplicity while achieving high noise removal performance.
Data Source
AI summary
An image processing device includes an encoder which infers latent variables from an input noisy image based on a preset noise. A denoiser removes the noise from the input noisy image to generate a denoising image. A decoder reconstructs a noisy image, using the inferred latent variable. The latent variables are learned on the basis of a difference between the reconstructed noisy image and the input noisy image.