Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects

Aashish Sharma, R. Tan
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引用次数: 19

Abstract

Most existing nighttime visibility enhancement methods focus on low light. Night images, however, do not only suffer from low light, but also from man-made light effects such as glow, glare, floodlight, etc. Hence, when the existing nighttime visibility enhancement methods are applied to these images, they intensify the effects, degrading the visibility even further. High dynamic range (HDR) imaging methods can address the low light and over-exposed regions, however they cannot remove the light effects, and thus cannot enhance the visibility in the affected regions. In this paper, given a single nighttime image as input, our goal is to enhance its visibility by increasing the dynamic range of the intensity, and thus can boost the intensity of the low light regions, and at the same time, suppress the light effects (glow, glare) simultaneously. First, we use a network to estimate the camera response function (CRF) from the input image to linearise the image. Second, we decompose the linearised image into low-frequency (LF) and high-frequency (HF) feature maps that are processed separately through two networks for light effects suppression and noise removal respectively. Third, we use a network to increase the dynamic range of the processed LF feature maps, which are then combined with the processed HF feature maps to generate the final output that has increased dynamic range and suppressed light effects. Our experiments show the effectiveness of our method in comparison with the state-of-the-art nighttime visibility enhancement methods.
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通过增加动态范围和抑制光效来增强夜间能见度
大多数现有的夜间能见度增强方法都集中在弱光下。然而,夜间图像不仅会受到弱光的影响,还会受到人造光的影响,如辉光、眩光、泛光灯等。因此,当现有的夜间能见度增强方法应用于这些图像时,它们会增强效果,进一步降低能见度。高动态范围(High dynamic range, HDR)成像方法可以解决低光和过曝光区域的问题,但不能消除光的影响,因而不能提高受影响区域的能见度。本文以单幅夜间图像作为输入,我们的目标是通过增加强度的动态范围来增强其可见性,从而提高低光区域的强度,同时抑制光效应(辉光、眩光)。首先,我们使用网络从输入图像估计相机响应函数(CRF)以线性化图像。其次,我们将线性化后的图像分解为低频(LF)和高频(HF)特征图,分别通过两个网络进行光效果抑制和噪声去除处理。第三,我们使用一个网络来增加处理过的低频特征图的动态范围,然后将其与处理过的高频特征图相结合,生成具有增加动态范围和抑制光效应的最终输出。我们的实验表明,与最先进的夜间能见度增强方法相比,我们的方法是有效的。
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