Compression Ratio Adapter for Variable-CR CS Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing end-to-end data-driven compressive sensing (CS) reconstruction frameworks are limited by their inability to adapt to variable compression ratios (CRs), requiring retraining for each CR, which is computationally costly and time-consuming, especially in resource-constrained IoT applications.
Innovation Solution
A compression ratio adapter (CRA) framework that uses an initial reconstruction network to generate an initial estimate based on a small portion of measurements, and approximates full measurements by re-sensing the initial estimate, allowing for flexible reconstruction across a range of CRs without modifying existing reconstruction models or requiring extensive additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing EDCSR frameworks are trained at each CR from scratch to achieve accurate reconstruction, then reconstruction accuracy is improved, but training time and computational cost increase significantly
Solution Approach 1:
The patent performs preliminary training of the EDCSR framework at a fixed reference CR during the offline phase. This pre-trained model serves as a foundation that can be adapted to variable CRs at runtime through the CRA mechanism, eliminating the need for retraining at each CR and thus resolving the contradiction between reconstruction accuracy and training time.
Solution Approach 2:
The CRA acts as an intermediary component that bridges the gap between the fixed-CR trained model and variable CR requirements. It takes the pre-trained model and the actual CR as inputs, generates appropriate measurement matrices, and produces reconstruction results adapted to the specific CR without requiring retraining, thus maintaining accuracy while avoiding time loss.
2Adaptability or versatility
If existing EDCSR frameworks are retrained for each variable CR to achieve adaptive reconstruction, then adaptability to variable CR is improved, but computational cost increases
Solution Approach 1:
The system performs the computationally expensive training operation in advance at a fixed CR, storing the trained model for later use. At runtime, only lightweight operations are performed through the CRA, which avoids repeated full training and thus reduces computational cost while maintaining adaptability to variable CRs.
Solution Approach 2:
The CRA serves as a computational intermediary that enables CR adaptation without full retraining. It uses the pre-trained model parameters and generates CR-specific measurement matrices through efficient operations, thereby achieving adaptability to variable CRs with minimal computational overhead.
3Device complexity
If EDCSR frameworks use fixed-resolution reconstruction to simplify the reconstruction process, then device complexity is reduced, but flexibility in resource-constrained applications is limited
Solution Approach 1:
The patent segments the reconstruction process into two independent parts: (1) a fixed, pre-trained EDCSR framework that handles the core reconstruction algorithm, and (2) a flexible CRA module that adapts to variable CRs. This segmentation allows the main reconstruction process to remain simple while the adaptation layer provides the needed flexibility.
Solution Approach 2:
The CRA acts as an intermediary layer between the fixed-resolution EDCSR framework and the variable CR requirements. It translates between the fixed framework output and the desired variable CR output through measurement matrix generation, thereby adding flexibility without increasing the complexity of the core reconstruction process.
Data Source
AI summary
A compression ratio (CR) adapter (CRA) for end-to-end data-driven compressive sensing (CS) reconstruction (EDCSR) frameworks is provided. EDCSR frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy for images and other signals. However, existing EDCSR frameworks cannot adapt to a variable CR. For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. Embodiments described herein present a CRA framework that addresses the variable CR problem generally for existing and future EDCSR frameworks with no modification to given reconstruction models nor enormous additional rounds of training needed. The CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of acquired image measurements. Subsequently, the CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with a re-sensed initial estimate.