DISR: Deep Infrared Spectral Restoration Algorithm for Robot Sensing and Intelligent Visual Tracking Systems

Abstract

Infrared imaging spectrometer (IRIS) often suffers from overlapped bands and random noises, which limit the precision of subsequent processing in robot vision sensing. To address this problem, we propose a novel Gabor transform-based infrared spectrum restoration method by successfully exploring the intrinsic structure of the clean IR spectrum from the degraded one. At first, a total variation (TV) regularized Gabor coefficients adjustment descriptor is designed and incorporated into the spectrum restoration model. Then, the proposed model is inferred via an efficient optimization approach based on split Bregman iteration method. Comprehensive experiments illustrate the significant and consistent improvements of the developed model over state-of-the-art approaches. The restored high-resolution spectrum can be utilized for detecting the different materials in the robot visual tracking systems.