X-ray machines are widely used for medical imaging and their cost is highly dependent on their image resolution.
Due to economic reasons, lower-resolution (lower-res) machines still have a lot of customers, especially in developing
economies. Software based resolution enhancement can potentially enhance the capabilities of the lower-res
machines without significantly increasing their cost hence, is highly desirable. In this work, we developed an
algorithm for X-ray image resolution enhancement. In this algorithm, the fractal idea and cross-resolution patch
matching are used to identify low-res patches that can be used as samples for high-res patch/pixel estimation.
These samples are then used to generate a prior distribution and used in a Bayesian MAP (maximum a posteriori)
optimization to produce the high-res image estimate. The efficacy of our algorithm is demonstrated by
Hongquan Zuo and Jun Zhang, "Resolution enhancement for x-ray images," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331L (Presented at SPIE Medical Imaging: February 15, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2250666.
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