A Novel Image Dehazing Algorithm via Adaptive Gamma-Correction and Modified AMEF

Abstract

Captured images are usually influenced by fog or haze. In reality, image dehazing is challenging. This paper proposes a modified artificial multiple-exposure image fusion (AMEF) algorithm to remove the haze from an image. In the algorithm, first, an adaptive gamma-correction transform with the mean and standard deviation values of each component of a hazy image is utilized to verify the intensities. Second, the homomorphic filtering algorithm is introduced into the Gaussian pyramid and Laplacian pyramid to compute the exposed accessible images. Last, a modified Laplacian filter method is presented to calculate the contrast of the exposed accessible images. Further, extensive experimental results demonstrate that the proposed algorithm has superior performance compared with that of some state-of-the-art methods, including higher contrast, richer details and a better visual effect in the dehazed image.INDEX TERMS Image dehazing, adaptive gamma-correction transformation, image fusion, Laplacian pyramid, modified AMEF.

Image haze removal algorithm based on nonsubsampled contourlet transform

“…In this experiment, the algorithm in this paper is selected to compare with three typical image dehazing algorithms. They are the dark channel prior algorithm proposed by He et al [8] , the Retinex enhancement algorithm proposed by Rui et al [2][3] , and the dehazing algorithm proposed by Galdran et al [15] based on artificial multi-exposure fusion AMEF. The results of dehazing are shown in Figures 9-12.…”