Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

Abstract

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Image dehazing with uneven illumination prior by dense residual channel attention network

“…Here, we try to segment the predicted transmission map into distant, intermediate and nearby scenes, because of the light variation with depth. Differing from Yoon et al 's [19] method, which utilises a threshold‐based strategy (fuzzy partition entropy) to partition scene regions, we combine fuzzy partition entropy with graph cuts to implement segmentation. Such a design not only considers the fuzzy intensity in a low‐contrast transmission map, but it also takes the spatial correlation into account.…”

“…In this section, we compare our proposed method with five state of the art image dehazing methods DCP‐based dehazing method proposed by He et al [11], WAPM‐based dehazing method proposed by Yoon et al [19], IASM‐based dehazing method proposed by Ju et al [20], AODN by Li et al [25], and PPDN proposed by Zhang et al [26]. Among these algorithms, DCP [11], WAPM [19] and IASM [20] are handcrafted prior based methods. While AODN [25] and PPDN [26] are CNN‐based methods.…”

“…1 b ). To better describe the imaging process, Yoon et al [19] proposed a wavelength‐adaptive physical model (WAPM) without using a even illumination assumption and estimate the transmission map by an image segmentation strategy. As a further step, Ju et al [20] proposed an improved atmospheric scattering model (IASM), where the atmospheric light and the varying scene light are calculated for addressing the uneven illumination problem.…”

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

“…Mathematical Problems in Engineering where ( , ) is the pixel index, ( , ) is the input hazy image, ∞ is the atmospheric light which is usually assumed to be a constant [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], ( , ) is the scene albedo, and ( , ) is the media transmission. In (1), the first term is named as direct attenuation, which describes how the scene radiance is attenuated by the suspended particles in the medium.…”

“…But the constant scattering coefficient may lead to unstable recovery quality, especially for inhomogeneous hazy image. Yoon et al [14] presented a spatially adaptive dehazing method that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. However, due to the insufficient wavelength information, this method is challenged when processing dense haze image.…”