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Multiframe super-resolution is the problem of reconstructing a single high-resolution (HR) image from several low-resolution (LR) versions of it. We assume that the original HR image undergoes different linear transforms, where each transform can be approximated as a set of linear shift-invariant transforms over different subregions of the HR image. The linearly transformed versions of the HR image are then downsampled, resulting in different LR images. Under the assumption of linearity, these LR images can form a basis that spans the set of the polyphase components (PPCs) of the HR image. We propose sampling rate diversity, where a secondary LR image, acquired by a secondary sensor of different (lower) sampling rate, is used as a reference to make known portions (subpolyphase components) of the PPCs of the reconstructed HR image. This setup allows for non-parametric reconstruction of the PPCs, where no knowledge of the underlying transforms is required, by solving for the expansion coefficients of the PPCs, in terms of the LR basis.