Filtering-Tikhonov regularization inversion for dynamic light scattering data with strong noise

摘要: Abstract The Tikhonov regularization is an effective method used for dynamic light scattering (DLS) data inversion. However, its inversion accuracy low the strong noise data. Based on filtering and technology, we propose a filter-Tikhonov-L method. To begin with, this uses cubical smoothing algorithm with five-point approximation to filter out of autocorrelation function (ACF). A new problem constructed by filtered ACF, then solved L-curve criterion. This combines advantages so that it has high accuracy. simulation particles particle size distribution (PSD) from 100 nm 700 nm was implemented studies. investigation shows great advantage in peak position, relative error, capability recognizing double peaks tolerance noises. experimental also verifies conclusion.