Image Enhancement via Bilateral Learning
This work addresses the need for efficient image enhancement for general users, but it is incremental as it builds on existing deep learning and bilateral grid approaches.
The paper tackles the problem of automatic image enhancement by combining convolutional neural networks with bilateral grids, resulting in quantitative and qualitative improvements over existing methods.
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.