Guided filter based on multikernel fusion

Xiang, Ruxi; Wu, Feng; Jiang, Xiaoyan; Xu, Qing

doi:10.1117/1.jei.26.3.033027

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…To improve the computational efficiency, He [15] proposed a guided filter with edge-maintaining characteristics instead of the soft matting algorithm to refine the transmission map. However, the edge smoothing technique of local filtering suffers from halo artifacts, so the improved algorithms [16][17][18][19][20][21] were proposed for this problem. (2) If the image scene contains a large area of the sky or a bright white object, the dark channel prior will invalid, resulting in severe distortion of the restored image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

Yang

Tang

2020

Algorithms

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The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.

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