Privacy-Enhanced Efficient Image Reconstruction: When Compressed Sensing Meets Meta-Learning

Compressed sensing (CS) is a powerful signal pro-cessing technique that can simultaneously realize signal sampling, compression, and lightweight encryption. However, traditional CS reconstruction algorithms often face challenges such as high signal sparsity requirements, poor reconstruction accuracy, and low efficiency. Meta-learning, which optimizes parameter selection and update strategies during iterations, can enhance the efficiency and convergence speed of CS reconstruction. In this article, we present a Meta-learning-based Compressed Sensing Reconstruction method in the Cloud (MetaCSRC), an end-to-end imaging paradigm designed to address these challenges. We develop an adaptive sampling network for CS sampling on the local client and a deep meta-learning convolutional network for CS loss optimization on the cloud server. By leveraging the cloud's sufficient computing and storage resources, the computationally intensive reconstruction process is offloaded from the clients. Additionally, we incorporate local differential privacy noise into the measurements before uploading them to the cloud server to enhance the security of measurement transmission. Experimental results demonstrate that MetaCSRC excels in both reconstruction speed and accuracy, while also providing enhanced privacy protection.