![]() Image haze removal via reference retrieval and scene prior. PDR-Net: perception-inspired single image dehazing network with refinement. Li C Y, Guo C L, Guo J C, Han P, Fu H Z, Cong R M. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. Li C Y, Guo J C, Cong R M, Pang Y W, Wang B. Signal Processing: Image Communication, 2019, 74: 253–265 Multi-scale optimal fusion model for single image dehazing. Haze removal for a single visible remote sensing image. IEEE Transactions on Image Processing, 2020, 29: 5481–5490 Unsupervised person reidentification via cross-camera similarity exploration. Single hazy image restoration using robust atmospheric scattering model. Computer Vision and Image Understanding, 2017, 165: 1–16ĭai C G, Lin M X, Wu X J, Zhang D. Haze visibility enhancement: a survey and quantitative benchmarking. Single image dehazing via self-constructing image fusion. In addition, our method outperforms the state-of-the-art methods qualitatively and quantitatively. ![]() Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast, color fidelity, and visibility. ![]() Then, we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion, where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids. We first propose a human visual system (HVS) inspired color correction model, which is effective for removing the color deviation on nighttime hazy images. Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime, we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm. The experimental results on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art methods in both qualitative and quantitative evaluations.Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime, which however, raises new challenges such as severe color distortion, more complex lighting conditions, and lower contrast. The Low-and-High frequency Fusion Subnetwork (L&HFSn) is used to fuse the low-frequency and high-frequency results to obtain the final dehazed image. The High-frequency Edge Enhancement Subnetwork (HEESn) is also proposed to enhance the edges and details while removing the noise. The Low-frequency Grid Dehazing Subnetwork (LGDSn) is proposed to effectively preserve the low-frequency structure while dehazing. An edge-preserving smoothing operator, a guided filter, is used to efficiently decompose the images into low-frequency image structure and high-frequency edges. To address these problems, we propose a novel Structure-transferring Edge-enhanced Grid Dehazing Network (SEGDNet) in this study. However, the previous dehazing methods usually have shortcomings such as poor brightness, color cast, removal of uncleanliness, halos, artifacts, and blurring. Under haze conditions, due to the scattering of water vapor and dust particles in the air, the sharpness of the image is seriously reduced, making it difficult for many computer vision systems, such as those for object detection, object recognition, surveillance, driver assistance, etc. The problem of image dehazing has received a great deal of attention in the computer vision community over the past two decades.
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