Artificial Intelligence-Based Digital Media Design Effect Enhancement Mechanism

Abstract

The advancement of AI technology has promoted the development speed of digital multimedia and brought a new experience to the digital media experience effect. In this paper, we aim to using artificial intelligence methods to enhance the digital media design experience. Specifically, we propose a method for low-light image enhancement using generative adversarial networks as a model framework. To better solve the problem, we design the following strategies in our proposed method. First, we preprocess the images into patches with a proper size. Second, we introduce the overall network structure of GAN. Third, we designed a multifeature extraction module with different sizes of convolution kernels to enhance the model’s ability for extracting features. Fourth, we propose a loss that combines the mean square error distance function with the adversarial error function to enable the model to learn the distributional features of the image. Finally, we verify the effectiveness of the proposed method on two datasets, namely, PASCAL VOC and MIT-Adobe FiveK. The results show that our proposed method performs well in the process of evaluation.