DISR: Deep Infrared Spectral Restoration Algorithm for Robot Sensing and Intelligent Visual Tracking Systems 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 transformbased 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 stateoftheart approaches. The restored highresolution spectrum can be utilized for detecting the different materials in the robot visual tracking systems.

Gaussian ScaleSpace Enhanced Local Contrast Measure for Small Infrared Target Detection Robust smalltarget detection plays an important role in the infrared (IR) search and track system, but it is still a challenge to detect small IR target under complex background. In this letter, an effective method inspired by the scalespace theory and the contrast mechanism of the human vision system is proposed. First, Gaussian scalespace (GSS) of an IR image is constructed by the convolution of a variablescale Gaussian function. Second, the gray features of the local image can be directly represented by downsampling in a scale image, and enhanced local contrast measure (ELCM) is defined to enhance small target and suppress complex background. Then, the saliency map is obtained by using maxpooling operation, and an adaptive threshold is adapted to segment real targets. Experimental results on a test set with three real IR sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression, and shows strong robustness under complex background. Especially, the proposed method has high computational efficiency, which can improve detection speed.