Multi-frame image super resolution using spatially weighted total variation regularisations

Image super resolution refers to a class of signal processing algorithms to post-process a captured image to obtain its high resolution version. Multi-frame super resolution synthesises high resolution image from multiple low resolution observations. Performance of super resolution algorithms are adversely affected by the noise present in the input images. To develop a noise robust multi-frame image super resolution, an objective function is formulated which contains a weighted data fidelity term and a regularisation term consisting of a bilateral total variation (BTV) term and structure tensor total variation (STV) term. Both BTV and STV are weighted appropriately in a per pixel basis in such a way that the BTV contributes more in smooth regions and STV contributes more on the edges. These terms ensure the continuity of edges and the smoothness of flat regions. An adaptive weighting scheme with the data fidelity term helps to select the reliable pixel alone in the reconstruction process. The proposed method is experimentally evaluated for its performance in real data and different types of noises.