用BP神经网络进行系统辨识的红外图像超分辨力复原方法

mages acquired from an infrared (IR) sensor typically suffer from poor spatial resolution due to the finite size of the lens that makes up the imaging system and the consequent imposition of the underlying diffraction limits. The lost frequency components beyond the diffraction-limited cutoff make the obtained images blur. Currently there are one kind of image processing schemes referred to as super-resolution algorithms available for solving of this problem, including Bayesian analysis methods, set theoretic methods, and Fourier domain techniques. But an estimate of the blur model parameters is essential in these methods. If incorrect blur parameters are chosen then the super-resolution results will be wrong. This work presents an original solution to the blur parameters identification problem in infrared image super-resolution. A back-propagation(BP) neural network is used for the blur parameters identification. In this method, we consider the modulation transfer function (MTF) of a