Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring
In the process of gradually upgrading aquaculture to the information and intelligence industries, it is usually necessary to collect images of underwater fish. In practical work, the quality of underwater images is often affected by water clarity and light refraction, resulting in most fish images n...
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2178 |
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| author | Zhuowei Wang Xiaoqi Jiang Chong Chen Yanxi Li |
| author_facet | Zhuowei Wang Xiaoqi Jiang Chong Chen Yanxi Li |
| author_sort | Zhuowei Wang |
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| description | In the process of gradually upgrading aquaculture to the information and intelligence industries, it is usually necessary to collect images of underwater fish. In practical work, the quality of underwater images is often affected by water clarity and light refraction, resulting in most fish images not fully displaying the entire fish body. Image inpainting helps infer the occluded fish image information based on known images, thereby better identifying and analyzing fish populations. When using existing image inpainting methods for underwater fish images, limited by the small datasets available for training, the results were not satisfactory. Lightweight Multi-scales Feature Diffusion (LMF-Diffusion) is proposed to achieve results closer to real images when dealing with image inpainting tasks from small datasets. LMF-Diffusion is based on guided diffusion and flexibly extracts features from images at different scales, effectively capturing remote dependencies among pixels, and it is more lightweight, making it more suitable for practical deployment. Experimental results show that our architecture uses only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>48.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the parameter of the guided diffusion model and produces inpainting results closer to real images in our dataset. Experimental results show that LMF-Diffusion enables the Repaint method to exhibit better performance in underwater fish image inpainting. Underwater fish image inpainting results obtained using our LMF-Diffusion model outperform those produced by current popular image inpainting methods. |
| format | Article |
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| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ee413b48565f4b0d87bd5b01f7ec1a4f2024-12-27T14:33:10ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011212217810.3390/jmse12122178Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish MonitoringZhuowei Wang0Xiaoqi Jiang1Chong Chen2Yanxi Li3School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Humanities and Science, Stanford University, Stanford, CA 94305, USAIn the process of gradually upgrading aquaculture to the information and intelligence industries, it is usually necessary to collect images of underwater fish. In practical work, the quality of underwater images is often affected by water clarity and light refraction, resulting in most fish images not fully displaying the entire fish body. Image inpainting helps infer the occluded fish image information based on known images, thereby better identifying and analyzing fish populations. When using existing image inpainting methods for underwater fish images, limited by the small datasets available for training, the results were not satisfactory. Lightweight Multi-scales Feature Diffusion (LMF-Diffusion) is proposed to achieve results closer to real images when dealing with image inpainting tasks from small datasets. LMF-Diffusion is based on guided diffusion and flexibly extracts features from images at different scales, effectively capturing remote dependencies among pixels, and it is more lightweight, making it more suitable for practical deployment. Experimental results show that our architecture uses only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>48.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the parameter of the guided diffusion model and produces inpainting results closer to real images in our dataset. Experimental results show that LMF-Diffusion enables the Repaint method to exhibit better performance in underwater fish image inpainting. Underwater fish image inpainting results obtained using our LMF-Diffusion model outperform those produced by current popular image inpainting methods.https://www.mdpi.com/2077-1312/12/12/2178aquaculturediffusion modelsinpaintingunconditional DDPM |
| spellingShingle | Zhuowei Wang Xiaoqi Jiang Chong Chen Yanxi Li Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring Journal of Marine Science and Engineering aquaculture diffusion models inpainting unconditional DDPM |
| title | Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring |
| title_full | Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring |
| title_fullStr | Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring |
| title_full_unstemmed | Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring |
| title_short | Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring |
| title_sort | lightweight multi scales feature diffusion for image inpainting towards underwater fish monitoring |
| topic | aquaculture diffusion models inpainting unconditional DDPM |
| url | https://www.mdpi.com/2077-1312/12/12/2178 |
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