2023/10/10 by Bowen Zhang, Zhang, Bowen, Zhijin Qin +3 · 2 citations
Computer Science · Engineering · #Energy Efficient Wireless Sensor Networks #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.06246
openalex publication_date 2023/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant compression ratios, which achieves higher imaging quality than the existing snapshot compressed imaging methods with the same sensing costs. In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself. Usually, different reconstruction algorithms are designed for applications in high dynamic range imaging, video compressive sensing, or motion debluring. This task-aware property inspires a semantic communication framework for programmable sensors. In this work, a policy-gradient based reinforcement learning method is introduced to achieve the explicit trade-off between the compression (or transmission) rate and the image distortion. Numerical results show the superiority of the proposed methods over existing baselines.