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A method for optimal linear super-resolution image restoration
A.I. Maksimov 1, V.V. Sergeyev 1,2

Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 1731 kB

DOI: 10.18287/2412-6179-CO-909

Pages: 692-701.

Full text of article: Russian language.

In this paper, we propose a super-resolution (pixel grid refinement) method for digital images. It is based on the linear filtering of a zero-padded discrete signal. We introduce a continuous-discrete observation model to create a reconstruction system. The proposed observation model is typical of real-world imaging systems - an initially continuous signal first undergoes linear (dynamic) distortions and then is subjected to sampling and the effect of additive noise. The proposed method is optimal in the sense of mean square recovery error minimization. In the theoretical part of the article, a general scheme of the linear super-resolution of the signal is presented and expressions for the impulse and frequency responses of the optimal reconstruction system are derived. An expression for the error of such restoration is also derived. For the sake of brevity, the entire description is presented for one-dimensional signals, but the obtained results can easily be generalized for the case of two-dimensional images. The experimental section of the paper is devoted to the analysis of the super-resolution reconstruction error depending on the parameters of the observation model. The significant superiority of the proposed method in terms of the reconstruction error is demonstrated in comparison with linear interpolation, which is usually used to refine the grid of image pixels.

digital images, super-resolution, continuous-discrete observation model, linear system, optimal recovery, impulse response, frequency response, optimal reconstruction error.

Maksimov AI, Sergeyev VV. A method for optimal linear super-resolution image restoration. Computer Optics 2021; 45(5): 692-701. DOI: 10.18287/2412-6179-CO-909.

The work was partly funded by the Russian Foundation for Basic Research under project No 19-31-90113 (“Introduction”, “General method of signal linear super-resolution”, “Continuous-discrete observation model”, “Optimal restoration of discrete values of a continuous signal”, “Optimal restoration of discrete values of a continuous signal – frequency domain analysis”, “Error of the optimal restoration” and  “Optimal restoration of a whole continuous signal”) and research project No 19-07-00474 (“Experimental research of the proposed method”).


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