In reconstruction based super-resolution, it is an important issue to up-sample and to merge the high-frequency
information contained in low-resolution images efficiently and without artifact. The conventional up-sampling methods,
which used for registering low-resolution data to high-resolution grid, have the difference between them and ideal upsampling
in observation modeling. In this paper, we analyze the difference and propose a new up-sampling and merging
method which is able to incorporate low-resolution images without loss of the high frequency data and minimizes the
artifact caused by data insufficiency. By this method, regions that the registered high frequency data do not cover are
naturally regularized without using complex regularizers. The experimental results show that the choice of up-sampling
significantly affects the quality of resulting images and the proposed up-sampling method gives better results compared
with conventional simple up-sampling method.
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