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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >FOGGY DEGRADED IMAGES: A RESTORATION APPROACH UTILIZING NEURAL NETWORK
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FOGGY DEGRADED IMAGES: A RESTORATION APPROACH UTILIZING NEURAL NETWORK

机译：有雾的降级图像：利用神经网络的恢复方法

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Atmospheric problems such as fog or dust reduce visibility on roads. Car cameras with a suitable image restoration technique can be used to enhance automotive vision in a misty (foggy) weather. Foggy images can be restored by using a suitable filter (de-noise filter) to reconstruct a clear image from its degraded version. Accordingly, this paper aims to find a fog filter to restore foggy images in real time (as a step toward the development of automotive vision in foggy weather). Supervised neural network (SNN) is used as a technique to restore a foggy image to its original version. Although training SNN is time consuming (during training phase), the process of applying the generated fog filter on a foggy image (for restoration) is a rapid operation. For generating a fog filter, SNN is trained offline through mapping between a foggy scene and its corresponding original scene. The weight matrix, which is obtained from training the SNN, represents a fog filter. In this paper, seven approaches utilizing different feature sets are proposed. Each approach presents different neural network (NN) architecture. Image features are extracted from spatial and transformed domains using discrete cosine transform (DCT). DCT is applied locally to suppress noise components while ?preserving the useful image ?content. The seven fog filters (resulting from training the seven NNs) are evaluated empirically, using Peak signal-to-noise ratio (PSNR), and perceptually (based on judgment of expert persons). Their performances are compared to specify the effective fog filter and to determine the feature set that best suits the NN technique for restoring foggy images. The recommended approach has demonstrated its efficiency and usefulness in restoring moderately foggy images in real time.
机译：诸如雾或灰尘之类的大气问题会降低道路上的视野。具有适合的图像恢复技术的车载摄像头可用于在有雾（有雾）的天气中增强汽车视觉。可以通过使用适当的过滤器（降噪过滤器）从降级版本重建清晰图像来恢复模糊图像。因此，本文旨在寻找一种雾过滤器，以实时恢复雾图像（这是朝着有雾天气发展汽车视觉的一步）。监督神经网络（SNN）被用作一种将模糊图像恢复到其原始版本的技术。尽管训练SNN非常耗时（在训练阶段），但是将生成的雾滤镜应用于模糊图像（以进行恢复）的过程非常快速。为了生成雾滤镜，通过在雾场景及其对应的原始场景之间进行映射来离线训练SNN。通过训练SNN获得的权重矩阵表示雾滤镜。本文提出了七种利用不同特征集的方法。每种方法都呈现不同的神经网络（NN）体系结构。使用离散余弦变换（DCT）从空间域和变换域中提取图像特征。 DCT局部应用以抑制噪声分量，同时保留有用的图像内容。使用峰值信噪比（PSNR）并根据感知（基于专家的判断）对7个雾滤镜（通过训练7个NN产生）进行经验评估。比较它们的性能以指定有效的雾滤镜，并确定最适合用于还原雾图像的NN技术的功能集。推荐的方法已经证明了其实时还原中等模糊图像的效率和有用性。

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