A real-time algorithm for single image dehazing is presented. The algorithm is based on calculation of local neighborhoods of a hazed image inside a moving window. The local neighborhoods are constructed by computing rank-order statistics. Next the dark-channel-prior approach is applied to the local neighborhoods to estimate the transmission function of the scene. By using the suggested approach there is no need for applying a refining algorithm to the estimated transmission such as the soft matting algorithm. To achieve high-rate signal processing the proposed algorithm is implemented exploiting massive parallelism on a graphics processing unit (GPU). Computer simulation results are carried out to test the performance of the proposed algorithm in terms of dehazing efficiency and speed of processing. These tests are performed using several synthetic and real images. The obtained results are analyzed and compared with those obtained with existing dehazing algorithms.
A local adaptive algorithm for single image dehazing is presented. The algorithm is able to estimate a dehazed image from an observed hazed scene by solving an objective function whose parameters are adapted to local statistics of the hazed image inside a moving window. The proposed objective function is based on a trade-off among several local rank order statistics of the dehazed signal and the mean-squared-error between the hazed and dehazed signals. In order to achieve a high-rate signal processing, the proposed algorithm is implemented in a graphics processing unit (GPU) exploiting massive parallelism. Experimental results obtained with a laboratory prototype are presented, discussed, and compared with those results obtained with existing single image dehazing methods in terms of objective metrics and computational complexity.