Restoration of multispectral images by the gradient reconstruction method and estimation of the blur parameters on the basis of the multipurpose matching model

Abstract

In this study, an original method for restoration of multispectral images by means of gradient reconstruction is proposed. The method uses a simple relationship between the gradient at the neighboring points in the distorted image and the gradient at distant points in the initial image. The result of restoration by the proposed algorithm excels the result obtained by the standard method based on the Wiener filtering. A new method for estimation of the parameters of the distorting motion-blur operator is also proposed. In this case, the blurred image is considered as a superposition of M shifted original images, and the autocorrelation convolution of the distorted image can be represented as a linear combination of M² mutual convolutions of several identical shifted images. Thus, the autocorrelation function of the distorted image is a straight line passing through the center, and the direction of this line and its length coincides with the parameters of the distorting operator. As compared to the best present-day algorithms, the proposed method exhibits higher accuracy of parameter estimation. In addition, computation of parameters with the use of this method takes much less time than with the use of popular estimation algorithms based on the Radon transform.