Single image dehazing using a multilayer perceptron

Sebastián Salazar-Colores; Ivan Cruz-Aceves; Juan-Manuel Ramos-Arreguin

doi:10.1117/1.JEI.27.4.043022

25 July 2018 Single image dehazing using a multilayer perceptron

Sebastián Salazar-Colores, Ivan Cruz-Aceves, Juan-Manuel Ramos-Arreguin

Author Affiliations +

Funded by: Sebastián Salazar-Colores, Consejo Nacional de Ciencia y Tecnología, Cátedras-CONACYT

Journal of Electronic Imaging, Vol. 27, Issue 4, 043022 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043022

Abstract

This paper presents an algorithm to improve images with hazing effects. Usually, the dehazing methods based on the dark channel prior make use of two different stages to compute the transmission map of the input image. The stages are the transmission map estimation and a transmission map refinement. However, the main disadvantage of these strategies is the trade-off between accurate restoration and computational time. The proposed method uses a multilayer perceptron to compute the transmission map directly from the minimum channel and a contrast stretching technique to improve the dynamic range of the restored image. The multilayer perceptron is trained in terms of mean squared error using a training set of 80 images. To evaluate the restoration quality, the metrics of peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index are used. The experimental results have proven that the proposed method achieves superior performance in terms of restoration quality (PSNR = 18.77, SSIM index = 0.8454) compared with nine state-of-the-art dehazing methods. In addition, based on the average computational time achieved by the proposed method (0.52 s using a test set of 46 images), it can be highly suitable for real-time applications.

Citation Download Citation

Sebastián Salazar-Colores, Ivan Cruz-Aceves, and Juan-Manuel Ramos-Arreguin "Single image dehazing using a multilayer perceptron," Journal of Electronic Imaging 27(4), 043022 (25 July 2018). https://doi.org/10.1117/1.JEI.27.4.043022

Received: 5 March 2018; Accepted: 3 July 2018; Published: 25 July 2018

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