Spatially adaptive high-resolution image reconstruction of low-resolution DCT-based compressed images
The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process, The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced, not only to reflect the relative amount of registration error in each low-resolution image, but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.