An image super-resolution algorithm based on Wiener filtering and wavelet transform

The super-resolution approach has attracted substantial attention in the field of image processing in view of its capability of providing higher resolution from low resolution image sequences. Interesting techniques have been developed and practical results have been obtained. However, in several theoretical investigations, good results are often corroborated by simulations, which limits the use of the developed techniques in practice. This paper presents a study on super-resolution algorithms based on Wiener filtering, that have low computational complexity compared to other optimization methods. Then, such techniques are applied to sequences of low resolution images decomposed by Haar wavelet coefficients. Experimental results are shown to verify the analytical predictions.