Nonlinear Adjustment Model with Integral and Its Application to Super Resolution Image Reconstruction

doi:10.11947/j.AGCS.2015.20140204

Abstract

Abstract: The process of super resolution image reconstruction is such a process that multiple observations are taken on the same target to obtain low resolution images, then the low resolution images are used to reconstruct the real image of the target, namely high resolution image. This process is similar to that in the field of surveying and mapping, in which the same target is observed repeatedly and the optimal values is calculated with surveying adjustment methods. In this paper, the method of surveying adjustment is applied into super resolution image reconstruction. A integral nonlinear adjustment model for super resolution image reconstruction is proposed at first. And then the model is parameterized with a quadratic function. Finally the model is solved with the least squares adjustment method. Based on the proposed adjustment method, the specific strategy of image reconstruction is presented. This method for super resolution image reconstruction can make quantitative analysis of the results, and avoid successfully ill-condition problem, etc. The results show that, compared to the traditional method of super resolution image reconstruction, this method has greatly improved the visual effects, and the PSNR and SSIM has also greatly improved, so the method is reliable and feasible.

Key words: super resolution image reconstruction, integral nonlinear adjustment model, gray function of object surface

CLC Number:

P207

ZHU Jianjun, FAN Donghao, ZHOU Cui, ZHOU Jinghong. Nonlinear Adjustment Model with Integral and Its Application to Super Resolution Image Reconstruction[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(7): 747-752.

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