Super-resolution interpolation with a quasi blur-hypothesis

We propose a new sparse-representation approach to super-resolution color-image interpolation. Whether an image to be interpolated actually undergoes blurs or not, the approach assumes that the image is blurred with a certain smoothing filter, and it first performs both deblurring and interpolation simultaneously so as to produce a provisional high-resolution image on the quasi blur-hypothesis, and then it blurs the provisional high-resolution image with the hypothesized blurring filter to produce a finally interpolated color-image. The quasi blur-hypothesis is introduced to enhance the spatially-regularization efficacy of the data-fidelity constraint in the variational problem of the super-resolution deblurring-and-interpolation. If a proper smoothing filter is chosen as the hypothesized blurring filter, the quasi blur-hypothesis approach can achieve state-of-the-art interpolation performance.