A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an image has a significant impact on the identification accuracy of pre-trained image classifiers. To address this problem, we propose a generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This network can effectively transform unclear images into clear and high-resolutiondoi:10.1109/access.2020.2982055 fatcat:byx6nmhyb5hx3jj6vqwj4kvjtm