Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

中文翻译：

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用于图像增强的对比度和合成多重曝光融合

为了提高智能手机的图像质量，已经取得了许多硬件和软件的进步，但不合适的照明条件仍然是图像质量的重大障碍。为了解决这个问题，我们提出了一种图像增强管道，包括合成多图像曝光融合和对不同照明条件鲁棒的对比度增强。在本文中，我们提出了一种生成合成多重曝光图像的新技术，通过根据输入图像的亮度使用不同的值对输入图像进行伽玛校正，以生成多个中间图像，然后通过应用对比度增强将其转换为最终的合成图像。我们观察到，我们提出的对比度增强技术侧重于图像的特定区域，从而产生不同的曝光、颜色和细节，以生成合成图像。视觉和统计分析表明，我们的方法在各种照明场景中表现更好，并且比最先进的方法获得更好的统计自然度和离散熵分数。