Optimized Binary Patterns by Gradient Descent for Ghost Imaging

Ghost imaging reconstructs images using a single-element photodetector; it performs by illuminating an object with binary modulation patterns. This technique has various advantages, including wide wavelength, noise robustness, and high measurement sensitivity. However, one challenge is the low image quality in undersampling. The examination of patterns intended to solve this issue. In ghost imaging, randomly generated or basis have been studied as patterns; however, undersampling, random exhibit robustness but quality, whereas are sensitive resolution. Thus, requires that simultaneously achieve resolution, robustness. study proposes method pattern optimization gradient descent binarization further improve quality. Numerical simulation experimental results show proposed approach offers noise.