Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging
This work addresses real-time imaging challenges in single-pixel imaging, though it is incremental as it builds on existing deep learning methods with specific regularizers.
The authors tackled the computational expense and noise sensitivity of single-pixel imaging by developing a modified deep convolutional autoencoder network with binary and orthogonality regularizers, achieving fast reconstruction times of ~3 ms per frame and robustness to noise at up to 6.25% compression ratio.
Single-pixel imaging (SPI) is a novel, unconventional method that goes beyond the notion of traditional cameras but can be computationally expensive and slow for real-time applications. Deep learning has been proposed as an alternative approach for solving the SPI reconstruction problem, but a detailed analysis of its performance and generated basis patterns when used for SPI is limited. We present a modified deep convolutional autoencoder network (DCAN) for SPI on 64x64 pixel images with up to 6.25% compression ratio and apply binary and orthogonality regularizers during training. Training a DCAN with these regularizers allows it to learn multiple measurement bases that have combinations of binary or non-binary, and orthogonal or non-orthogonal patterns. We compare the reconstruction quality, orthogonality of the patterns, and robustness to noise of the resulting DCAN models to traditional SPI reconstruction algorithms (such as Total Variation minimization and Fourier Transform). Our DCAN models can be trained to be robust to noise while still having fast enough reconstruction times (~3 ms per frame) to be viable for real-time imaging.