Hybrid CNN-Transformer Reconstruction for Compressive Sensing
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Solution Overview
Problem
Existing compressive sensing (CS) methods face challenges in efficiently reconstructing images due to high computational complexity and the limitations of convolutional neural networks (CNNs) in capturing both local and global features, leading to suboptimal performance in image restoration.
Innovation Solution
A hybrid network, CSformer, integrates convolutional neural networks (CNNs) for local spatial information and transformers for global context, using a dual-branch structure to fuse local and global features under different resolutions, enhancing representation learning for adaptive sampling and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks (CNNs) are used for image reconstruction, then local spatial information is captured effectively, but the ability to capture global context and long-range dependencies is limited
Solution Approach 1:
The patent merges CNN and transformer architectures into a hybrid network where CNN extracts local features and transformer captures global context. The two architectures are combined in an encoder-decoder structure with feature fusion layers that integrate local and global representations, resolving the contradiction between local feature extraction and global context modeling.
Solution Approach 2:
The patent creates a composite neural network architecture that combines different computational paradigms (convolutional and attention-based) into a unified model. This composite structure leverages the strengths of both CNNs (local processing efficiency) and transformers (global context capture) to achieve superior image reconstruction performance.
2Adaptability or versatility
If transformer architecture is used for global context modeling, then long-range dependencies are captured effectively, but computational complexity increases significantly
Solution Approach 1:
The patent segments the image into patches and processes them through a hierarchical transformer architecture. By dividing the global context modeling into localized window-based attention operations and hierarchical feature processing, the computational complexity is reduced from O(N^2) to O(N) while maintaining global context capture capability.
Solution Approach 2:
The patent introduces hierarchical processing dimensions to the transformer architecture, organizing attention operations across multiple scales and resolutions. This dimensional organization allows efficient computation by processing features at different granularities, reducing the overall computational burden while preserving global context information.
3Measurement precision
If deep neural networks are used for compressive sensing reconstruction, then reconstruction quality improves, but training data requirements and model complexity increase
Solution Approach 1:
The patent applies local quality by using CNNs for local feature extraction in the encoder and decoder parts of the network. This localized processing approach reduces model complexity compared to using transformers throughout, while maintaining reconstruction quality through efficient local pattern recognition and feature learning.
Data Source
AI summary
A method for adaptive reconstruction of a compressively sensed data. The method contains the steps of receiving sensed data; conducting an initial reconstruction to the sensed data to obtain a plurality of first reconstruction patches; by a reconstruction module, conducting a progressive reconstruction to the sensed data to obtain a plurality of second reconstruction patches; summing the plurality of second reconstruction patches with the a plurality of first reconstruction patches to obtain final patches; and merging the final patches to obtain a reconstructed data. The progressive reconstruction further contains concatenating transformer features and convolution features to obtain the second reconstruction patches. The invention provides a hybrid network for adaptive sampling and reconstruction of CS, which integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning.