Journal of Safety Science and Resilience (Mar 2024)

Single image defogging via multi-exposure image fusion and detail enhancement

  • Wenjing Mao,
  • Dezhi Zheng,
  • Minze Chen,
  • Juqiang Chen

Journal volume & issue
Vol. 5, no. 1
pp. 37 – 46

Abstract

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Outdoor cameras play an important role in monitoring security and social governance. As a common weather phenomenon, haze can easily affect the quality of camera shooting, resulting in loss and distortion of image details. This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion (AMEF) algorithm. First, the foggy image is adaptively exposed, and the fused image is subsequently obtained via multiple exposures. The fusion weight is determined by the saturation, contrast, and brightness. Finally, the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image. It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information, effectively improving the defogging ability.

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