Flexible FTIR Spectral Imaging Enhancement for Industrial Robot Infrared Vision Sensing

Abstract

Infrared (IR) spectral imaging sensing is a powerful visual technique for industrial material recognition in robot vision systems. However, the imaging sensing data have issues of random noise and band overlap. Resolution enhancement is usually the first step in the preprocessing procedure of industrial robot vision sensing. In this article, we develop a resolution-enhancement algorithm with total variation (TV) constraints for the degraded Fourier transform IR (FTIR) spectrum due to overlap and noise degradation in the robot vision sensing. The kernel function is calculated using the spectrometer imaging systems and Fourier optical theory. The proposed model not only can remove noises effectively but also can estimate the kernel function because of the adaptive TV as constraint regularization. This model is examined by a set of simulated FTIR spectra with the Poisson noises and a series of real FTIR spectra. The proposed model is compared with the other state-of-the-art methods in terms of performance. Experimental results demonstrate that the proposed approach can split the overlap band effectively while the spectral structure details are retained satisfactorily. The enhanced high-resolution imaging spectrum data can raise the robot vision sensing accuracy in industrial intelligent systems.