Discrete cosine transform based regularized high-resolution image reconstruction algorithm

While high-resolution images are required for various applications, aliased low-resolution images are only available due to the physical limitations of sensors. We propose an algorithm to reconstruct a high-resolution image from multiple aliased low-resolution images, which is based on the generalized deconvolution technique. The conventional approaches are based on the discrete Fourier transform (DFT) since the aliasing effect is easily analyzed in the frequency domain. However, the useful solution may not be available in many cases, i.e., the underdetermined cases or the insufficient subpixel information cases. To compensate for such ill-posedness, the generalized regularization is adopted in the spatial domain. Furthermore, the usage of the discrete cosine transform (DCT) instead of the DFT leads to a computationally efficient reconstruction algorithm. The validity of the proposed algorithm is both theoretically and experimentally demonstrated. It is also shown that the artifact caused by inaccurate motion information is reduced by regularization.