Optimization of RGB image spectral reconstruction based on radial basis function networks

Abstract

The Spectral Reconstruction (SR) algorithm attempts to recover hyperspectral information from RGB camera responses. This estimation problem is usually formulated as a least squares regression, and because the data is noisy, Tikhonov regularization is reconsidered. The degree of regularization is controlled by a single penalty parameter. This paper improves the traditional cross validation experiment method for the optimization of this parameter. In addition, this article also proposes an improved SR model. Unlike common SR models, our method divides the processed RGB space into different numbers of neighborhoods and determines the center point of each neighborhood. Finally, the adjacent RGB data and spectral data of each center point are used as input and output data for the Radial Basis Function Network (RBFN) model to train the SR regression of each RGB neighborhood. This article selects MRAE and RMSE to evaluate the performance of the SR algorithm. Through comparison with different SR models, the methods proposed in this article have significant performance improvements.