Application of Compressive Sensing theory in infrared imaging systems

摘要

Compressive Sensing (CS) theory shows that if a signal is sparse under a certain basis, it can be recovered from a small number of measurement samples. These measurements can be significant departures from the traditional notion of a pixel associated with a single detector element in a focal plane array. In this work, we study the problem of how different sampling methods affect image recovery. In our study, a 2D Fourier transform is used as the measurement method. Spatial frequency sampling is accomplished using three different methods, uniform random method, 2D Gaussian method with different variances and a LineMask method. Infrared images from a stationary surveillance camera are recovered from these collected samples. Peak Signal-to-Noise Ratio (PSNR) is used to evaluate the quality of recovered images. Our simulation results show that, with the same number of collected measurement samples, both LineMask and 2D Gaussian methods offer better image recovery results than the random method. For the 2D Gaussian method, the image recovery results improve slightly as the variance of Gaussian sampling function decreases. The recovery result of the LineMask method is between the best and worst cases from the Gaussian method. Our results show that while the CS technique allows images to be recovered from randomly chosen measurement samples, the method used to collect the measurement samples does affect the signal recovery quality. Choosing a proper sampling method can optimize recovery using the CS technique.