A super-resolution reconstruction algorithm of infrared pedestrian images via compressed sensing

摘要

Pedestrian detection is the major task of many infrared surveillance system. Due to the technical limitation of sensor or the high cost of advanced hardware, the resolution of infrared images is usually low, which is not capable of meeting the high quality requirement of various applications. Compressed sensing capturing and represents compressible signals at a sample rate significantly below the Nyquist rate, is considered as a new framework for signal reconstruction based on the sparsity and compressibility. Thus, the compressed sensing theory enlightens a computational way to reconstruct a high resolution image on the basis of a sparse signal, i.e. the low resolution image. The proposed method use low resolution and high resolution infrared pedestrian images to train an over-complete dictionary through K-SVD algorithm, by which the pedestrian are sparsely well-represented. Two distant infrared cameras in the same scene are used to capture high and low resolution image to make sure same pedestrian pair is sparsely represented under the over-complete dictionary. Therefore the similarities are learning between input low resolution image patches and high resolution image patches. The popular greedy algorithm Orthogonal Matching Pursuit (OMP) is utilized for sparse reconstruction, providing optimal performance and guaranteeing less computational cost and storage. We evaluate the quality of reconstructed image employing root mean square error and peak signal to noise. The experimental results show that the reconstructed images preserve wealthy detailed information of pedestrian, and have low RMSE and high PSNR, which are superior to the traditional super-resolution methodologies.