26 May 2017 Image dehazing based on partitioning reconstruction and entropy-based alternating fast-weighted guided filters
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Abstract
A robust image dehazing algorithm based on the first-order scattering of the image degradation model is proposed. In this work, there are three contributions toward image dehazing: (i) a robust method for assessing the global irradiance from the most hazy-opaque regions of the imagery is proposed; (ii) more detailed depth information of the scene can be recovered through the enhancement of the transmission map using scene partitions and entropy-based alternating fast-weighted guided filters; and (iii) crucial model parameters are extracted from in-scene information. This paper briefly outlines the principle of the proposed technique and compares the dehazed results with four other dehazing algorithms using a variety of different types of imageries. The dehazed images have been assessed through a quality figure-of-merit, and experiments have shown that the proposed algorithm effectively removes haze and has achieved a much better quality of dehazed images than all other state-of-the-art dehazing methods employed in this work.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
YuanYu Wang, Peter Yuen, "Image dehazing based on partitioning reconstruction and entropy-based alternating fast-weighted guided filters," Optical Engineering 56(5), 053111 (26 May 2017). https://doi.org/10.1117/1.OE.56.5.053111 . Submission: Received: 22 March 2017; Accepted: 8 May 2017
Received: 22 March 2017; Accepted: 8 May 2017; Published: 26 May 2017
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