Confidence map based super-resolution reconstruction
Magnetic Resonance Imaging and Computed Tomography usually provide highly anisotropic image data, so that the resolution in the slice-selection direction is poorer than in the in-plane directions. An isotropic high-resolution image can be reconstructed from two orthogonal scans of the same object. While combining the different data sets, all input data are usually equally weighted, without considering the fidelity level of each input information. In this paper we introduce a novel super-resolution method, which considers the fidelity level of each input data by introducing an adaptive confidence map. Experimental results on simulated and real data sets have shown the improved accuracy of reconstructed images, whose resolution approximate the original in-plane resolution in all directions. The quality of the reconstructed high resolution image was improved for noiseless input data sets, and even in the presence of different noise types with a low peak signal to noise ratio.