Super-resolution of License-plates Using Weighted Interpolation of Neighboring Pixels from Video Frames

Recognizing the license plate from a set of low-resolution video frames using an Optical Character Recognition system (OCR) is a very challenging task. OCR systems fail to properly work in this condition. The use of high-quality cameras is a costly solution to this situation. To overcome this problem, we propose a weighted interpolation method that enhances the resolution of the license plate, using consecutive frames of a video. For this purpose, first, we register the low-resolution video frames of the license plate to the reference license plate in two steps. In the first step, a coarse registration is performed by matching the SURF features. Then a fine registration on the license plate region is performed using the phase correlation technique. After registration, the reference image of the license plate is up-sampled to the desired scale. We propose a method for estimating the intensity of pixels in the up-sampled image with an unknown value. In this method, we use a weighted averaging strategy to estimate the intensity of unknown pixels using the neighboring pixels from video frames.  The obtained super-resolution is suitable for OCR. Experimental results show that applying the proposed method on low-resolution frames of the license plate, improves the quality of the license plate significantly.