Efficient OCT Image Reconstruction via Deep Compressed Sensing for Intelligent IoT Devices

Optical Coherence Tomography (OCT) is a high-resolution 3D imaging technique widely used in medical diagnostics. However, its increasing resolution leads to massive data volumes, creating challenges in storage, transmission, and real-time access—issues that are further intensified in Internet of Things (IoT) environment. In such settings, where portable OCT devices operate at the edge in remote or resource-limited environments, efficient and secure data handling is critical for real-time diagnostics, remote consultations, and cloud integration. Existing compression methods often struggle to balance efficiency, image quality, and the preservation of diagnostic features, limiting their applicability in constrained IoT infrastructures. To address these challenges, we propose OCT-CSNet, a deep compressed sensing (CS) model tailored for OCT images. By integrating deep learning with traditional CS theory, our model leverages non-parametric prior features to guide both compression and reconstruction stages. In the compression stage, the dual-path compression module efficiently captures the inherent sparsity and structural coherence of OCT images while preserving critical differential information. In the reconstruction stage, we fuse the Proximal Gradient Descent (PGD) algorithm with a deep neural network, incorporating data-driven priors into the iterative optimization process. Extensive experiments show that OCT-CSNet can improve the reconstruction quality under different compression ratios while retaining semantic features that are critical to downstream tasks. In addition, its high computational efficiency and low memory footprint also provide potential for its application in IoT-driven imaging systems.