Vehicle license plate super-resolution using soft learning prior

Abstract

This paper proposes a new algorithm to perform single-frame image super-resolution (SR) of vehicle license plate (VLP) using soft learning prior. Conventional single-frame SR/interpolation methods such as bi-cubic interpolation often experience over-smoothing near the edges and textured regions. Therefore, learning-based methods have been proposed to handle these shortcomings by incorporating a learning term so that the reconstructed high-resolution images can be guided towards these models. However, existing learning-based methods employ a binary hard-decision approach to determine whether the prior models are fully relevant or totally irrelevant. This approach, however, is inconsistent with many practical applications as the degree of relevance for the prior models may vary. In view of this, this paper proposes a new framework that adopts a soft learning approach in license plate super-resolution. The method integrates image SR with optical character recognition (OCR) to perform VLP SR. The importance of the prior models is estimated through relevance scores obtained from the OCR. These are then incorporated as a soft learning term into a new regularized cost function. Experimental results show that the proposed method is effective in handling license plate SR in both simulated and real experiments.