Large-Scale Hyperspectral Image Restoration with Two-Layer Graphs
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Solution Overview
Problem
Existing hyperspectral image (HSI) denoising methods struggle with noise removal due to ignoring spatial and spectral correlations, leading to poor restoration performance, especially in large-scale HSIs, and they often lose boundary information and texture details, making them unsuitable for scalable applications.
Innovation Solution
A large-scale HSI restoration method using graph signal processing and superpixel segmentation, which constructs a two-layer graph architecture with a skeleton graph for superpixel correlation and local graphs for pixel similarity, applying a distributed denoising algorithm through sub-graph optimization with graph Laplacian regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional band-by-band denoising methods are applied to HSI, then the processing complexity is reduced, but the restoration performance deteriorates due to ignoring spectral correlations
Solution Approach 1:
The patent segments the HSI into multiple sub-blocks spatially and processes each sub-block independently using local graph construction. This segmentation allows parallel processing while maintaining local correlations, resolving the contradiction between computational complexity and restoration performance by balancing global spectral-spatial relationships with localized processing efficiency
Solution Approach 2:
The patent employs a nested graph structure where a global graph captures overall spectral-spatial correlations and local graphs capture fine-grained pixel relationships within sub-blocks. This nested architecture enables multi-scale feature extraction, allowing the system to maintain high restoration performance while managing computational complexity through hierarchical processing
2Reliability
If non-local methods are used for HSI denoising, then global correlations are considered, but boundary information is lost due to square patch-based strategy
Solution Approach 1:
The patent applies different processing strategies to different regions: global graph processing for capturing overall correlations and local graph processing for preserving boundary details in each sub-block. This local quality approach ensures that boundary regions maintain their structural integrity while still benefiting from global correlation modeling, preventing information loss at critical boundaries
3Measurement precision
If centralized operators are applied to large-scale HSI, then restoration accuracy is maintained, but scalability deteriorates
Solution Approach 1:
The patent divides large-scale HSI into multiple independent sub-blocks that can be processed in parallel. Each sub-block undergoes denoising using localized graph processing, enabling the system to scale to large images without requiring excessive memory or computational resources, thus maintaining scalability while preserving restoration accuracy through local graph Laplacian regularization
Solution Approach 2:
The patent transforms the traditional 2D spatial processing into a graph-based representation that captures multi-dimensional correlations (spatial, spectral, and structural). This dimensional transformation allows the system to handle large-scale data efficiently by leveraging the inherent structure of hyperspectral images across multiple dimensions, improving both scalability and accuracy
4Loss of information
If existing superpixel segmentation methods are applied to HSI, then boundary preservation is improved, but restoration performance deteriorates due to ignoring PWS characteristics and superpixel similarity
Solution Approach 1:
The patent merges superpixel segmentation with graph signal processing by constructing graphs where nodes represent superpixels and edges capture similarity relationships. This combination integrates boundary preservation from superpixel methods with correlation modeling from graph processing, simultaneously achieving both boundary integrity and high restoration performance through unified optimization
Solution Approach 2:
The patent modifies the graph construction parameters to incorporate superpixel similarity metrics and piecewise smoothness characteristics. By adjusting the adjacency matrix weights based on superpixel relationships and PWS properties, the system adapts the graph structure to preserve boundaries while maintaining restoration accuracy, resolving the contradiction between boundary preservation and restoration performance
Data Source
AI summary
Provide is a novel mixed-noise removal method for HSI with large size. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called a skeleton graph, is a rough graph constructed by using the modified k-nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation, which can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph, consists of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs.