Structure-Aware Image Denoising and Noise Variance Estimation
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Solution Overview
Problem
Conventional image denoising techniques often cause blurring along edges and lack an accurate method for estimating noise variance, making them ineffective in real-world scenarios where noise levels vary significantly from image to image.
Innovation Solution
Structure-aware image denoising techniques that select reference patches based on image structure and compute weights for denoising operations, combined with noise variance estimation using a map of patches to identify uniform regions for accurate variance estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional denoising techniques are applied to remove noise from images, then noise removal is achieved, but blurring occurs along edges and structural details are lost
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local structural characteristics. Structure-aware weights are computed for each pixel based on its local neighborhood, allowing aggressive denoising in uniform regions while preserving edges and fine details in structurally complex regions. This local adaptation resolves the contradiction by making denoising strength dependent on local image content.
Solution Approach 2:
The patent segments the image into different regions based on structural similarity and variance characteristics. By identifying uniform regions versus edge/texture regions through patch comparison and variance analysis, the method applies appropriate denoising intensity to each segment, preventing edge blurring while effectively removing noise from homogeneous areas.
2Ease of operation
If noise variance parameter is set to a default value or user input, then the denoising process can proceed, but accurate estimation of noise variance becomes difficult when noise levels vary greatly across different images
Solution Approach 1:
The patent implements automatic noise variance estimation that operates without user intervention. The system analyzes the image content itself, computing variance metrics from patch comparisons and identifying uniform regions to derive the noise variance parameter. This self-service approach eliminates the need for user input while achieving accurate, image-specific noise variance estimation.
Solution Approach 2:
The patent uses feedback from image analysis to automatically adjust the noise variance parameter. By examining local variance across the image and comparing patch similarities, the system derives an accurate noise variance estimate that adapts to the specific characteristics of each image, resolving the issue of inaccurate default values across different images.
3Manufacturing precision
If structure-aware patch selection and weighting are used to preserve image structure, then edge preservation is improved, but computational complexity increases
Solution Approach 1:
The patent computes structure-aware weights for a selected subset of reference patches rather than all possible patches. By limiting the search to a manageable number of candidate patches and computing weights only for those, the method achieves sufficient structure preservation while keeping computational complexity at acceptable levels for practical implementation.
Data Source
AI summary
Structure aware image denoising and noise variance estimation techniques are described. In one or more implementations, structure-aware denoising is described which may take into account a structure of patches as part of the denoising operations. This may be used to select one or more reference patches for a pixel based on a structure of the patch, may be used to compute weights for patches that are to be used to denoised a pixel based on similarity of the patches, and so on. Additionally, implementations are described to estimate noise variance in an image using a map of patches of an image to identify regions having pixels having a variance that is below a threshold. The patches from the one or more regions may then be used to estimate noise variance for the image.