Video super resolution using contourlet transform and bilateral total variation filter

This paper introduces a new approach for video super resolution problem. To this end Compressive Sensing (CS) theory along with contourlet transform has been used. In CS framework the signal is assumed to be sparse in a transform domain. An approach has been suggested using this fact in which contourlet domain is used as the transform domain and a CS algorithm helps to find the high resolution frame. A post processing step is applied afterward to the estimated outputs to increase the quality. The post processing step consists of a deblurring term and a Bilateral Total Variation (BTV) filter for increasing the consistency. This method helps to relax the conditions on hardware and increase the quality of the video after capturing, in fact the quality of the video streams in consumer applications can be increased even the capturing device represents the scene in a low resolution format. Experimental results show significant improvement over existing super resolution methods in both objective and subjective quality.