Nowadays, due to advanced digital imaging technologies and internet accessibility to the public, the number of generated digital images has increased dramatically. Thus, the need for automatic image enhancement techniques is quite apparent. In recent years, deep learning has been used effectively. Here, after introducing some recently developed works on image enhancement, an image enhancement system based on convolutional neural networks is presented. Our goal is to make an effective use of two available approaches, convolutional neural network and bilateral grid. In our approach, we increase the training data and the model dimensions and propose a variable rate during the training process. The enhancement results produced by our proposed method, while incorporating 5 different experts, show both quantitative and qualitative improvements as compared to other available methods.
翻译:目前,由于先进的数字成像技术和向公众开放互联网,生成的数字图像的数量急剧增加,因此,自动图像增强技术的必要性相当明显,近年来,已经有效地利用了深层次的学习。在这里,在引入了一些最近开发的图像增强工作之后,介绍了一个基于进化神经网络的图像增强系统。我们的目标是有效利用两种可用的方法,即进化神经网络和双边网络。在我们的方法中,我们增加了培训数据和模型层面,并提出了培训过程中的可变率。我们拟议方法产生的增强成果,同时吸收了5名不同的专家,显示了与其他可用方法相比,在数量和质量上都有所改善。