Single image dehazing is a technique used to remove the effect of haze from an image captured in poor weather conditions. Due to the scattering of particles, a captured image suffers from low visibility and contrast. Besides, scattering also adds nonlinear noise to the captured image. Existing image dehazing methods improve the visibility of the hazy image. However, these methods significantly generate artifacts such as halo at the depth discontinuities, blocking, and color aliasing in the sky regions. Some methods addressed this problem, but these methods introduce other issues such as loss of details, blurring effects, and oversaturation in the dehazed image. This paper proposes a method using superpixels and ensemble nonlinear regression to estimate the transmission that improves the visibility of a hazy image without any artifact. Conventional machine learning methods require a vast amount of haze-free and hazy images of different haze concentrations to train the model. The use of superpixels offers less number of training examples and also helps in reducing halo artifacts. The ensemble nonlinear regression predicts the transmission for a superpixel in such a way that the recovered image looks more natural, especially in the sky regions. The proposed method is evaluated by the various distortion parameters on real-world challenging and synthetic hazy images. The qualitative and quantitative analysis in experimental results proves that the proposed method is superior to that of state-of-the-art dehazing methods.

中文翻译：

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超像素和集成神经网络的无失真图像去雾

单幅图像去雾是一种用于从恶劣天气条件下拍摄的图像中消除雾霾影响的技术。由于粒子的散射，捕获的图像的可见度和对比度较低。此外，散射还为捕获的图像增加了非线性噪声。现有的图像去雾方法提高了模糊图像的可见性。然而，这些方法会显着产生伪影，例如深度不连续处的光晕、阻塞和天空区域的颜色混叠。一些方法解决了这个问题，但这些方法引入了其他问题，例如去雾图像中的细节丢失、模糊效果和过饱和。本文提出了一种使用超像素和集成非线性回归来估计透射率的方法，该方法可以在没有任何伪影的情况下提高朦胧图像的可见性。传统的机器学习方法需要大量不同雾度浓度的无雾和有雾图像来训练模型。超像素的使用提供了较少数量的训练示例，还有助于减少光晕伪影。集成非线性回归以恢复图像看起来更自然的方式预测超像素的透射率，尤其是在天空区域。所提出的方法通过现实世界具有挑战性和合成模糊图像的各种失真参数进行评估。实验结果的定性和定量分析证明所提出的方法优于最先进的去雾方法。超像素的使用提供了较少数量的训练示例，还有助于减少光晕伪影。集成非线性回归以恢复图像看起来更自然的方式预测超像素的透射率，尤其是在天空区域。所提出的方法通过现实世界具有挑战性和合成模糊图像的各种失真参数进行评估。实验结果的定性和定量分析证明所提出的方法优于最先进的去雾方法。超像素的使用提供了较少数量的训练示例，还有助于减少光晕伪影。集成非线性回归以恢复图像看起来更自然的方式预测超像素的透射率，尤其是在天空区域。所提出的方法通过现实世界具有挑战性和合成模糊图像的各种失真参数进行评估。实验结果的定性和定量分析证明所提出的方法优于最先进的去雾方法。所提出的方法通过现实世界具有挑战性和合成模糊图像的各种失真参数进行评估。实验结果的定性和定量分析证明所提出的方法优于最先进的去雾方法。所提出的方法通过现实世界具有挑战性和合成模糊图像的各种失真参数进行评估。实验结果的定性和定量分析证明所提出的方法优于最先进的去雾方法。