Acquisition super resolution from infrared images using proposed techniques

This paper suggests three novel proposed techniques for super resolution (SR) infrared (IR) images. The first algorithm is relied on the image acquisition model, which considers benefits of the sparse representations of low resolution (LR) and high resolution (HR) patches using Bi-cubic interpolation and minimum mean square error (MMSE) estimation. This estimation in HR image prediction stage providing a scheme can be interpreted as a feed forward neural network. The second scheme is based on up-sampling for IR images using Second Kernel Lanczos Interpolation (SKLI).The third scheme is depended on up-sampling for IR images using Third Kernel Lanczos Interpolation (TKLI).This technique is typically used to increase the sampling rate of a digital signal, or to shift it by a fraction of the sampling interval. The performance metrics are Peak Signal-To-Noise Ratio (PSNR) and computation time. Simulation results prove that the success of three presented techniques in acquisition high resolution of SR IR images. By comparing the three presented algorithms with Regularized Interpolation (REI) and least squares Interpolation (LSI) schemes of IR images. It is clear that the second suggested technique gives superior than REI and LSI schemes from point views PSNR and computation time. On the other hand the third presented technique is the best algorithms from point views PSNR and computation time to other techniques.