Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise. In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one. Different from existing bio-inspired algorithm, the proposed method doesn't use any training sequences, we depend on a novel chain of contrast enhancement and denoising algorithms without using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.

翻译：由于许多智能监控系统在夜间进行目标检测与识别的准确率较低，夜间图像的质量至关重要。相较于对应的日间图像，夜间图像具有亮度低、对比度低且噪声高的特征。本文提出一种基于生物启发的图像增强算法，将低照度图像转换为更明亮清晰的图像。与现有生物启发算法不同，所提方法无需使用任何训练序列，而是依赖一种新颖的对比度增强与去噪算法链，且未采用任何形式的递归函数。我们的方法能大幅提升夜间图像的亮度与对比度，同时有效抑制噪声。随后我们通过实际实验与仿真实验对算法进行测试。两种实验结果均表明，所提算法相较于对比对算法、Meylan算法及Retinex算法具有显著优势。