篇名 |
A Super-resolution Reconstruction Method for Underwater Fish Images with Feedback Networks
|
---|---|
並列篇名 | A Super-resolution Reconstruction Method for Underwater Fish Images with Feedback Networks |
作者 | Xiu-Juan Wang、Liang Mao、Ji Wang、Wen-Chao Gong |
英文摘要 | Video surveillance is commonly used for production process monitoring in aquaculture. Because underwater video images are prone to blurring and unclear detail texture, this paper proposes a super-resolution network reconstruction method with a feedback network. The reconstruction effect is enhanced by a feedback network in the process of mapping low-resolution images to high-resolution images, which is implemented with a constrained recurrent neural network (RNN) to process deep feedback information. However, the shallow features are fed to the feedback net-work module to generate deeper features through multiple upsampling and downsampling and progressive refinement, which give the network structure an early reconstruction capability and are conducive to more realistically reconstructing high-resolution images. A course learning strategy is introduced to make the network applicable to more complex tasks and improve its robust-ness. We construct a training set of 800 images and validation and test sets of 10 images each using images of marine net fish as the research object. The method of this paper is validated on the public dataset Set14 and a self-built dataset. The proposed method outperforms other methods in both subjective and objective evaluations on the public and self-built datasets. Good foundation for high-definition monitoring of aquaculture is laid.
|
起訖頁 | 109-119 |
關鍵詞 | aquaculture、fish images、super-resolution reconstruction、feedback networks、course learning |
刊名 | 電腦學刊 |
期數 | 202210 (33:5期) |
DOI |
|
QR Code | |
該期刊 上一篇
| A Low-Power Remote Information Monitoring System of Cold Chain Logistic Based on NB-IoT |
該期刊 下一篇
| Particle Swarm Optimization with Long and Short Term Memory in Feature Selection |