Multiframe combination and blur deconvolution of video data

In this paper we present a technique that may be applied to surveillance video data to obtain a higher-quality image from a sequence of lower-quality images. The increase in quality is derived through a deconvolution of optical blur and/or an increase in spatial sampling. To process sequences of real forensic video data, three main steps are required: frame and region selection, displacement estimation, and original image estimation. A user-identified region-of-interest (ROI) is compared to other frames in the sequence. The areas that are suitable matches are identified and used for displacement estimation. The calculated displacement vector images describe the transformation of the desired high-quality image to the observed low quality images. The final stage is based on the Projection Onto Convex Sets (POCS) super-resolution approach of Patti, Sezan, and Tekalp. This stage performs a deconvolution using the observed image sequence, displacement vectors, and an a priori known blur model. A description of the algorithmic steps are provided, and an example input sequence with corresponding output image is given.