Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

Jesus A. Valderrama; Víctor H. Díaz-Ramírez; Vitaly Kober; Enrique Hernandez

doi:10.1117/12.2188491

10 September 2015 Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

Jesus A. Valderrama, Víctor H. Díaz-Ramírez, Vitaly Kober, Enrique Hernandez

Proceedings Volume 9598, Optics and Photonics for Information Processing IX; 95981G (2015) https://doi.org/10.1117/12.2188491
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

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.

Citation Download Citation

Jesus A. Valderrama, Víctor H. Díaz-Ramírez, Vitaly Kober, and Enrique Hernandez "Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior", Proc. SPIE 9598, Optics and Photonics for Information Processing IX, 95981G (10 September 2015); https://doi.org/10.1117/12.2188491

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KEYWORDS

Image processing

Air contamination

Image restoration

Image transmission

Light scattering

Signal processing

Graphics processing units

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