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access icon free Statistical multidirectional line dark channel for single-image dehazing

  • Author(s): Sebastián Salazar Colores 1 ; Eduardo Ulises Moya-Sánchez 2 ; Juan-Manuel Ramos-Arreguín 3 ; Eduardo Cabal-Yépez 4
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  • Source: Volume 13, Issue 14, 12 December 2019, p. 2877 – 2887
    DOI:  10.1049/iet-ipr.2018.6403 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 30/10/2018, Accepted 08/05/2019, Revised 09/04/2019, Published 12/05/2019

Outdoor scenes often contain atmospheric degradation, such as fog or haze, which deteriorate the performance of tracking, autonomous driving and surveillance systems, among others, making dehazing methods an area of considerable interest. However, some dehazing techniques are computationally demanding, generating a trade-off between time-consumption and restoration quality. A new method is proposed for improving outdoor images taken with haze effects while making them less time-consuming. The proposed method was inspired by the Radon transform and tailored for dehazing images by computing the dark channel, in addition to using statistical computations and a heuristic approach to avoid saturated areas. The results obtained were subjected to a reduced-reference image quality dehazing assessment, a full-reference metrics Structural SIMilarity (SSIM) index and peak signal-to-noise ratio (PSNR) over real-world and synthetic outdoor images. The results demonstrate that the proposed method presents an adequate balance between new visible edges, increased gradient and saturated pixels, in addition to obtaining at least a 5% increase in the SSIM index and, a 16% increase in the PSNR index, as well as being 5.37 times faster than the four dehazing methods recently introduced in the literature.

Inspec keywords: image resolution; image restoration; statistical analysis; fog; image enhancement; Radon transforms

Other keywords: statistical computations; saturated areas; restoration quality; reduced-reference image quality dehazing assessment; fog; outdoor scenes; haze effects; single-image dehazing; statistical multidirectional line dark channel; synthetic outdoor images; dehazing images; dehazing techniques; autonomous driving; Radon transform; full-reference metrics Structural SIMilarity index; atmospheric degradation; surveillance systems; dehazing methods

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Other topics in statistics; Other topics in statistics

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