Modification of existing genetic algorithm optimization image restoration method through convolutional neural networks
In recent years, image restoration has been gaining increasing attention due to the widespread usage of image-based information such as in complex classification models used throughout multiple industries. Due to image restoration algorithms, the abundance of data has not only increased but slightly...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2021
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/148377 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In recent years, image restoration has been gaining increasing attention due to the widespread usage of image-based information such as in complex classification models used throughout multiple industries. Due to image restoration algorithms, the abundance of data has not only increased but slightly damaged images are no longer a source of concern to use as data. Furthermore, image restoration has various other uses such as for medical imaging, astronomical imaging to forensic science and even recreational uses. Genetic algorithm (GA) is widely applicable to multiple industries due to its optimization abilities. Despite it being an emerging domain, GA has been applied to image restoration projects as well. Specifically, through the use of genetic algorithm optimization, images are able to be restored with structure-priority. In the existing method, structural information is extracted using canny edge detection. However, this project seeks to optimize the structural information obtain through the use of convolutional neural networks instead. |
|---|