Colorimetric characterization of color imaging systems using a multi‐inputPSO‐BPneural network

Abstract

Most commonly used camera characterization methods do not use a deep learning‐based artificial neural network approach at present. This article proposes a colorimetric characterization method for color imaging systems based on the multi‐input particle swarm optimization backpropagation neural network. Combined with a particle swarm optimization algorithm for global search and a 19‐input vector, this method not only overcomes the effects of local extrema on the multi‐input backpropagation neural network, but also improves the accuracy of the common input backpropagation neural network. Images of a ColorChecker SG chart were collected using a Canon EOS 1000D camera for experimental verification, and the color differences were used to evaluate the characterization results. The results show that the color differences of the multi‐input particle swarm optimization backpropagation neural network (structure: 19‐7‐3) model are substantially better than those of the multi‐input backpropagation neural network (structure: 19‐7‐3) and common input backpropagation neural network (structure: 3‐4‐3) models. Its performance is close to that of the weighted nonlinear regression model. The multi‐input particle swarm optimization backpropagation neural network is hence an effective method for colorimetric characterization with good prediction accuracy.

Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

“…It can be seen from Formulas (1) and (2) that there exists a nonlinear conversion between the ( R , G , B ) and the ( X , Y , Z ). In fact, different methods have been used to model the conversion from the RGB space to the XYZ space [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Among them, polynomial transforms and the BP artificial neural network (BP-ANN) are the most popular.…”

“…Until now, different kinds of colorimetric characterization methods have been developed, including polynomial transforms [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], artificial neural networks (ANN) [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ], and look-up tables [ 24 ]. In 2016, Gong et al [ 12 ] demonstrated a color calibration method between different digital cameras with ColorChecker SG cards and the polynomial transform method and achieved an average color difference of 1.12 (CIEDE2000).…”

“…In 2016, Gong et al [ 12 ] demonstrated a color calibration method between different digital cameras with ColorChecker SG cards and the polynomial transform method and achieved an average color difference of 1.12 (CIEDE2000). In 2022, Xie et al [ 18 ] proposed a colorimetric characterization method for color imaging systems with ColorChecker SG cards based on a multi-input PSO-BP neural network and achieved average color differences of 1.53 (CIEDE2000) and 2.06 (CIE1976L*a*b*). However, the methods mentioned above were mainly used for cameras with standard color space such as an sRGB system.…”

Support vector machine-based method for colorimetric characterization of computer-controlled liquid crystal displays

“…In addition, Liao and others have many meaningful articles in the field of color. 30,31 Poljicak et al 32 proposed a radial basis function (RBF) ANN for colorimetric characterization. The accuracy of colorimetric characterization can be optimized by choosing an adequate basis function.…”

“…This method greatly decreased the mathematical computation in color appearance prediction. In addition, Liao and others have many meaningful articles in the field of color 30 , 31 . Poljicak et al 32 .…”