In order to solve the blurred image superresolution reconstruction problem, the fractional calculus is combined with projection onto convex sets (POCS) to reconstruct superresolution images. Using a fractional calculus operator to get the reference frame, through scale invariant feature transform (SIFT) matching, along with the fractional point spread function, and the imaging blurred images problem is solved by POCS, and the superresolution reconstruction is implemented for noised or blurred images. Comparing with other superresolution algorithms, experiments show that POCS with FCoptimized SIFT has good results in several objective evaluation indeces. Especially, when the origin lowresolution image is blurred, the algorithms based on contour stencils or sparse representation will increase the degree of blurring of the image, and the image obtained has obvious improvement in clarity and better reconstruction effect.