Single image dehazing using a multilayer perceptron

• Published in: Journal of electronic imaging 2018-07, Vol.27 (4), p.043022-043022
• Main Authors: Salazar-Colores, Sebastián, Cruz-Aceves, Ivan, Ramos-Arreguin, Juan-Manuel
• Format: Article
• Language: English
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.27.4.043022

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.