Real-Time Low-Light Imaging in Space Based on the Fusion of Spatial and Frequency Domains

Abstract

Due to the low photon count in space imaging and the performance bottlenecks of edge computing devices, there is a need for a practical low-light imaging solution that maintains satisfactory recovery while offering lower network latency, reduced memory usage, fewer model parameters, and fewer operation counts. Therefore, we propose a real-time deep learning framework for low-light imaging. Leveraging the parallel processing capabilities of the hardware, we perform the parallel processing of the image data from the original sensor across branches with different dimensionalities. The high-dimensional branch conducts high-dimensional feature learning in the spatial domain, while the mid-dimensional and low-dimensional branches perform pixel-level and global feature learning through the fusion of the spatial and frequency domains. This approach ensures a lightweight network model while significantly improving the quality and speed of image recovery. To adaptively adjust the image based on brightness and avoid the loss of detailed pixel feature information, we introduce an adaptive balancing module, thereby greatly enhancing the effectiveness of the model. Finally, through validation on the SID dataset and our own low-light satellite dataset, we demonstrate that this method can significantly improve image recovery speed while ensuring image recovery quality.