Coded-Aperture imaging based on random code and Backpropagation neural network

Coded-aperture radiation imaging technology has important application value in the field of space radiation detection. A coded-aperture imaging algorithm based on random code and Backpropagation neural network (BPNN) is proposed for high quality imaging of x-rays and gamma rays in space environments. The experimental results show that BPNN can improve the signal-to-noise ratio of the reconstructed image when applied to the reconstruction process of coded-aperture imaging based on random code. Therefore, it has a good application prospect in the field of space coded-aperture imaging.