Principles of Controlling the Apparatus Function in Super-Resolution Problem

When controlling the Apparatus Function (AF), the size of definition domain of the AF O and the sampling step and conditionality of the AF must be chosen so that its inverse function pR=pО-1 obtains a minimum norm. The compensation of the AF O distortions in the measured images is realized point-by-point (without using the Fourier Transform in convolution). The computer of the device uses the resolving function pR, selected by the controlling procedure, for achieving super-resolution in images. Such controlled super-resolution is demonstrated on the Martian images.

Principles of Controlling the Apparatus Function in Super-Resolution Problem

When controlling the Apparatus Function (AF), the size of definition domain of the AF O and the sampling step and conditionality of the AF must be chosen so that its inverse function pR=pО-1 obtains a minimum norm. The compensation of the AF O distortions in the measured images is realized point-by-point (without using the Fourier Transform in convolution). The computer of the device uses the resolving function pR, selected by the controlling procedure, for achieving super-resolution in images. Such controlled super-resolution is demonstrated on the Martian images.

Principles of Controlling the Apparatus Function in Super-Resolution Problem

When controlling the Apparatus Function (AF), the size of definition domain of the AF O and the sampling step and conditionality of the AF must be chosen so that its inverse function pR=pО-1 obtains a minimum norm. The compensation of the AF O distortions in the measured images is realized point-by-point (without using the Fourier Transform in convolution). The computer of the device uses the resolving function pR, selected by the controlling procedure, for achieving super-resolution in images. Such controlled super-resolution is demonstrated on the Martian images.

Application of Turchin's method of statistical regularization

During analysis of experimental data, one usually needs to restore a signal after it has been convoluted with some kind of apparatus function. According to Hadamard's definition this problem is ill-posed and requires regularization to provide sensible results. In this article we describe an implementation of the Turchin's method of statistical regularization based on the Bayesian approach to the regularization strategy.