Generalized Sobel Filters for gradient estimation of distorted images
Antonino Furnari, Giovanni Maria Farinella, Arcangelo Ranieri Bruna, Sebastiano Battiato · 2015
In this paper we tackle the problem of correctly estimating the gradient of distorted images. The proper estimation of the gradient in the presence of distortion is of great interest due to the large number of applications relying on wide angle cameras (e.g., in surveillance, automotive, robotics). To this aim we propose the Generalized Sobel Filters (GSF), a family of adaptive Sobel filters able to correctly estimate the gradient of distorted images. To assess the performances of the proposed method, we acquired a benchmark dataset of high resolution images belonging to different categories which are relevant to application domains where the gradient estimation is usually employed. We build an objective evaluation pipeline and perform experiments which show that our method outperforms the state-of-the-art.