Abstract: 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.

Key words: low-light image enhancement, residual network, InceptionNet V1, convolutional neural network, feature fusion