Abstract
Computational resolution enhancement (superresolution) is generally regarded as a memory-intensive process due to the large matrix-vector calculations involved. In this paper, a detailed study of the structure of the $n^2\times n^2$ superresolution matrix is used to decompose the matrix into nine matrices of size $l^2\times l^2$, where l is the upsampling factor. As a result, previously large matrix-vector products can be broken into many small, parallelizable products. An algorithm is presented that utilizes the structural results to perform superresolution on compact, highly parallel architectures such as field-programmable gate arrays.
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