Valid motion estimation for super-resolution image reconstruction
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In this thesis, a block-based motion estimation algorithm suitable for Super-Resolution (SR) image reconstruction is introduced. The motion estimation problem is formulated as an energy minimization problem that consists of both a data and regularization term. To handle cases when motion estimation fails, a block-based validity method is introduced, and is shown to outperform all other validity methods in the literature in terms of hybrid de-interlacing. By combining the validity metric into the energy minimization framework, it is shown that 1) the motion vector error is made less sensitive to block size, 2) a more uniform distribution of motion-compensated blocks results, and 3) the overall motion vector error is reduced. The final motion estimation algorithm is shown to outperform several state-of-the-art motion estimation algorithms in terms of both endpoint error and interpolation error, and is one of the fastest algorithms in the Middlebury benchmark. With the new motion estimation algorithm and validity metric, it is shown that artifacts are virtually eliminated from the POCS-based reconstruction of the high-resolution image.