Real-Time Imaging in Low Light Conditions (UC 2020-236-1)

Prof. Luat Vuong and colleagues from the University of California, Riverside have developed a method for imaging in low light and low signal-to-noise conditions. This technology works by using a dense neural network to reconstruct an object from intensity-only data and efficiently solves the inverse mapping problem without performing iterations with each image and without deep learning schemes. This network operates without learned stereotypes with low computational complexity, low reconstruction latency, decreased power consumption, and robust resistance to disturbances compared to current imaging technologies. Figures are pictured below in Images/Media Gallery: Figure 1:Theoretical/simulation accuracy for multi-vortex arrays – 3,5,7 correspondingly using the dense single layer neural net, in comparison to convolutional NN and a single layer NN using conventional imaging. The SNR is provided for the conventional imaging scheme. Venkata Krishnamurty venkatak@ucr.edu (951) 827-4967