Super-Resolution Based on Residual Dense Network for Agricultural Image
Guojiang Liu, Jiangshu Wei, Yu Michael Zhu, Yi Ming Wei · Journal of Physics Conference Series · 2019
Abstract With the rapid development of convolutional neural network in image processing, its image processing performance is becoming more and more outstanding. With the development of agricultural informatization, our demand for effectively accessing to agricultural information is also increasing. For example, greenhouse image monitoring equipment for crops is only in a low level. In this paper, we propose a method to apply image super resolution to the processing of greenhouse monitoring images, and use residual dense network to enhance the learning of global and local features in agricultural images. Therefore, more detailed information can be obtained from the low resolution images. Experiments on the data sets are made from the greenhouse agriculture images, and experimental results show that our method can effectively enhance the quality of agricultural monitoring images. It can better meet the needs of agricultural monitoring.