Deep learning based standard rainbow inversion algorithm for retrieving droplet refractive index and size

Traditional standard rainbow inversion algorithms for retrieving droplet size and refractive index require timeconsuming complex data pre-processing and multiple calculations, while optimized. This paper proposes a novel standard rainbow signal inversion algorithm based on residual networks and multi-objective regression. The algorithm requires only simple data pre-processing and is tested on a simulated data set covering representative signals from droplets with refractive indices of 1.33-1.35 and sizes of 60-240 mu m. The average absolute error of the refractive index and average relative error of the droplet size are 9.16 x 10-5 and 0.55%, respectively. Experimental validations were carried under laboratory conditions on streams of mondisperse droplets and the robustness of the algorithm was verified under two conditions. In addition to its satisfactory inversion accuracy, the speed of each signal inversion is three times faster than traditional algorithms. The proposed algorithm meets the requirements of engineering applications and scientific research in terms of accuracy and real-time performance.