基于端到端双网络的低照度图像增强方法

由于环境的不确定性，捕获的图像存在亮度低、对比度低和信息丢失严重等问题，且利用现存算法增强后的图像存在曝光过度问题，不能满足计算机视觉任务的输入要求。针对此问题，提出了基于端到端双网络的低照度图像增强方法，该网络由Inception网络模块与URes-Net模块组成。首先利用Retinex理论合成低照度图像样本；然后运用双网络模型进行特征提取、特征融合与重建，根据测试集的损失不断调整参数以优化模型，最终使双网络模型具有较高的低照度图像增强能力。实验结果表明，所提方法的PSNR和SSIM的均值分别为28.659 8 dB和0.896 6,亮度、对比度显著提高，获得的图像更加符合人类视觉，优于其他先进的低照度图像增强方法。

A low-light image enhancement methodbased on an end-to-end dual network

Objective: Due to the uncertainty of the environment, the captured image has some problems, such as low brightness, low contrast, serious information loss and so on. Moreover, the image enhanced by the existing algorithms has the problem of over exposure, which cannot meet the input requirements of computer vision tasks. Methods: To solve this problem, a low illumination image enhancement method based on end-to-end dual network is proposed, which consists of Inception module and URes-Net module. Firstly, the low illumination image samples are synthesized by Retinex theory, and then the dual network model is used for feature extraction, feature fusion and reconstruction. According to the loss of the test set, the parameters are continuously adjusted to optimize the model. Finally the dual network model has high low illumination image enhancement ability. Results: the experimental results show that the mean values of PSNR and SSIM are 28.659 8 db and 0.896 6 respectively, which are better than other advanced low illumination image methods. Conclusion: compared with other method, the brightness and contrast of this method are significantly improved, and the image obtained is more in line with the visual sense.