Image Enhancement via Bilateral Learning

• URL: http://arxiv.org/abs/2112.03888v1
• Date: Tue, 7 Dec 2021 18:30:15 GMT
• Title: Image Enhancement via Bilateral Learning
• Authors: Saeedeh Rezaee and Nezam Mahdavi-Amiri
• Abstract summary: This paper presents an image enhancement system based on convolutional neural networks. Our goal is to make an effective use of two approaches, convolutional neural network and bilateral grid. The enhancement results produced by our proposed method, while incorporating 5 different experts, show both quantitative and qualitative improvements.
• Score: 1.4213973379473654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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