Accelerated unsupervised filtering for the smoothing of road pavement surface imagery

An accelerated formulation of the Unsupervised Information-theoretic Adaptive Image Filtering (UINTA) method is presented. It is based on a parallel implementation of the algorithm, using the Open Computing Language (OpenCL), while maintaining the precision and efficiency of the original method, which are briefly discussed focusing on the respective computational complexities. The experimental computational efficiency is compared with the one obtained using the standard implementation, highlighting the significant improvement of computational times achieved with the proposed one. This new implementation is tested for the smoothing of road pavement surface images, for which the original method had been previously applied, showing the clear advantage of its use.