EISRP: Efficient infrared signal restoration processing for object tracking in human-robot interaction

Abstract

Infrared (IR) imaging spectral signal can accurately distinguish different industrial metal materials in the robot vision sensing. The novel IR imaging system can assist the robot limb to gasp the object in the human-robot interaction processing. However, the low-resolution problem is often existed in the IR spectral signals. To solve this issue, we present a novel IR spectral deconvolution method via total variation regularization for robust spectral signal recognition. To reveal the properties of high-resolution IR spectrum, the histogram of spectral gradients is computed and fitted by the logarithmic function. Then, the low-resolution and high-resolution IR spectra can be distinguished by total variation-regularized prior property. To optimize the non-convex deconvolution model, alternating minimization algorithm is introduced to ensure that the instrument function and high-resolution spectral signals converge quickly. The performance of the proposed method is evaluated on the simulated IR spectrum and real spectrum. A large number of quantitative and qualitative experimental results verify that the proposed method can obtain robust results and significantly outperform the existing state-of-the-art spectral deconvolution method. The high-resolution IR spectrum promotes the development of IR spectral recognition in human-robot interaction.