A Novel UAV Sensing Image Defogging Method

Abstract

Imaging of unmanned aerial vehicle easily suffer from haze, resulting in decline in the quality of required remote sensing images. The influence brings great challenges in later analysis and process. Although dark channel prior has acquired substantial achievements, some limitations, including imprecise estimation of atmospheric light, color distortion, and lower brightness of defogging image, still exist. In this article, to target these drawbacks, a novel defogging method for single image is proposed. First, a novel atmospheric scattering model is proposed to define the more accurate atmospheric light by introducing an adaptive variable strategy. Next, unlike traditional dark channel prior, a novel estimation method is presented by fusing dark and light channels to estimate more precise atmospheric light and transmittance. Then, we adopt the gray image corresponding to color image as a guidance image to refine the transmittance to further decrease the time complexity. Aiming at the region of low transmittance, a novel compensation function is created to improve the region of low transmittance and avoid color distortion. Moreover, a simple and effective calculation method is proposed to determine parameters in compensation function. Finally, the clear remote sensing image is established by an improved atmospheric scattering model. Extensive experiments on real-world datasets demonstrate that the proposed method outperforms several other state-of-the-art approaches both on subjective and objective quality evaluations. Index Terms-Dark channel prior (DCP), defogging, remote sensing image, unmanned aerial vehicle (UAV). I. INTRODUCTION I N RECENT years, owing to the advantages of agility, economy, convenience, and adaptability [1], unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in disaster and environmental monitoring [2], agriculture [3], archaeology [4], disaster relief [5], target detection [6], and other fields. However, the imaging equipment of UAV is easily affected by haze, resulting in decline in the quality of required remote sensing images and leading to the difficulty in extracting effective information of images in later process, which seriously affects the analysis and judgment of visual system [7], [8]. Consequently, defogging of remote sensing image has important significance in UAV practical applications [9]-[11].

Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method

“…This may lead to the overall whiteness, contrast reduction, and the color deviation of the captured image [ 19 ]. Moreover, the targets may be covered by fog [ 20 ], which will result in poor detection performance. Smoke has similar physical characteristics to fog (both have a white color and foggy shape), which also results in reducing the accuracy of the forest fire detection.…”

A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection

“…The reversing image obscured or polluted by weather conditions such as raindrops or haze will affect the accuracy of reversing driving. Therefore, many scholars are committed to image restoration processing technology to overcome the image degradation caused by rain [9][10][11][12][13][14][15] or fog [16][17][18][19][20][21][22][23][24] problem. In 2014, Shaodi You et al proposed an idea which is to use long-range trajectories to discover the motion and appearance features of raindrops locally along the trajectories to detect raindrops and to utilize patches indicated to remove adherent raindrops [9].…”

“…In 2020, Di Fan et al proposed an image defogging algorithm to overcome the problems of low contrast and low definition of fog degraded image by three steps: firstly, transforming images from Red, Green, Blue (RGB) space to Hue, Saturation, Intensity (HSI) space and using two-level wavelet transform to extract features of image brightness components; secondly, using the K-Singular Value Decomposition (K-SVD) algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image; thirdly, using the nonlinear stretching approach for saturation component improves the brightness of the image and then converts from HSI space to RGB color space to get the defog image [20]. In 2020, Gao Tao et al proposed a novel defogging method that overcomes some limitations including imprecise estimation of atmospheric light, color distortion by both defining the more accurate atmospheric light by introducing the adaptive variable strategy and fusing dark channel and light channel to estimate more precise atmospheric light and transmittance [21]. In 2021, Sabiha Anan et al proposed a framework which is using a binary mask formulated to do segmentation of the sky and non-sky region by flood fill algorithm, and is using Contrast Limited Adaptive Histogram Equalization (CLAHE) and modified Dark Channel Prior (DCP) to restore the foggy sky and non-sky parts, respectively [22].…”

Afdn: Attention-Based Feedback Dehazing Network For Uav Remote Sensing Image Haze Removal

“…(1), these parameters J , A and () tx are all unknown, which is a severely ill-posed problem for single image dehazing. Therefore, the prior-based methods [9][10][11][12], such as dark channel prior (DCP) [9], must leverage various priors or assumptions related to haze to estimate transmission maps and airlight from single hazy images. The prior-based methods strongly rely on their proposed priors or assumptions.…”