JOURNAL ARTICLE

Single Image Haze Removal Using Weak Dark Channel Prior

Abstract

Haze removal or dehazing has been a challenge in the field of image restoration. Recently, He et al.. proposed a single image dehazing scheme based on an interesting statistical prior called the dark channel prior (DCP). By the DCP, two parameters in the haze image model, the atmospheric light and the transmission map, can be estimated easily. Consequently, the DCP scheme has attracted much attention in this field. Note that the DCP scheme relies on the block-based dark channel which is considered as a strong DCP assumption. In this paper, a pixel-based dark channel is introduced through which the atmospheric light and the transmission map are estimated. The pixel-based dark channel is considered as a weak DCP (WDCP) since its statistical property is not as strong as that in the block-based dark channel. With a similar manner in the DCP scheme, the atmospheric light is estimated through the pixel-based dark channel. To make the pixel-based dark channel feasible in the transmission map estimation, an adaptive scaling factor for the initial transmission map is employed and the pixel-based dark channel is applied as the guide image in the transmission map refinement by the guided image filtering. Furthermore, an objective assessment is used to evaluate the proposed WDCP scheme and the compared DCP scheme. Simulation results indicate that the proposed WDCP scheme is more efficient, 24.30 times faster than the DCP scheme on average. Moreover, the proposed WDCP scheme is of better subjective visual quality than the DCP scheme and the employed objective assessment generally agrees with the results in the given examples.

Keywords:
Haze Channel (broadcasting) Pixel Computer science Block (permutation group theory) Transmission (telecommunications) Computer vision Artificial intelligence Image (mathematics) Scheme (mathematics) Physics Mathematics Telecommunications Geometry

Metrics

8
Cited By
0.72
FWCI (Field Weighted Citation Impact)
16
Refs
0.71
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image Enhancement Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Fusion Techniques
Physical Sciences →  Engineering →  Media Technology
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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