The method for reconstruction and restoration of super-resolution images from sequences of low-resolution images presented here is an extension of the algorithm proposed by Irani and Peleg ('Improving resolution by image registration', CVGIP: Graphical Models and Image Processing, 1991, 53, (3), pp. 231-239). After estimating the projective transformation parameters between the image sequence frames, the observed data is rearranged into a sequence with only quantized sub-pixel translations. The super-resolution reconstruction is an iterative process, in which a high-resolution image is initialized and iteratively improved. The improvement is achieved by back-projecting the errors between the translated low-resolution images and the respective images obtained by simulating the imaging system. The imaging system's point-spread function (PSF) and the back-projection function are first estimated with a resolution higher than that of the super-resolution image. The two functions are then decimated so that two banks of polyphase filters are obtained. The use of the polyphase filters allows exploitation of the input data without any smoothing and/or interpolation operations. The presented experimental results show that the resolution improvement is better than the results obtained with Irani and Peleg’s algorithm.

 

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