In dark or poorly lit environments, it is often difficult for the naked eye to distinguish low-light-level images because of low brightness, low contrast and noise, and it is difficult to perform subsequent image processing (such as video surveillance and target detection). To solve these problems, this paper proposes a low-light-level image enhancement algorithm based on deep learning. First, the low-light-level image is segmented into several super-pixels, and the noise level of each super-pixel is estimated by the ratio of the local standard deviation to the local gradient. Then, the image is inverted and smoothed by a BM3D filter, and the structural filter adaptive method is used to obtain complete images without noise but with the correct texture. Finally, the noise-free image and texture-complete images are applied to the integrated network, which can not only enhance the contrast but also effectively prevent the over-enhancement of the contrast. The experimental results show that this method is superior to traditional methods in terms of both subjective and objective evaluation, and the peak signal–noise ratio and improved structural similarity are 31.64 dB and 91.2%, respectively.

中文翻译：

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基于集成网络的微光图像增强算法

在黑暗或光线不足的环境中，由于亮度低、对比度低、噪声低，肉眼往往难以分辨低照度图像，难以进行后续的图像处理（如视频监控和目标检测） ）。针对这些问题，本文提出了一种基于深度学习的低照度图像增强算法。首先，将低照度图像分割成若干个超像素，通过局部标准差与局部梯度的比值来估计每个超像素的噪声水平。然后，通过BM3D滤波器对图像进行倒置和平滑处理，并采用结构滤波器自适应方法获得无噪声但纹理正确的完整图像。最后，将无噪声图像和纹理完整图像应用于集成网络，这样不仅可以增强对比度，还可以有效防止对比度的过度增强。实验结果表明，该方法在主观和客观评价方面均优于传统方法，峰值信噪比和改进的结构相似度分别为31.64 dB和91.2%。