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