Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery
In rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and...
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MDPI AG
2025-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1729 |
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| author | Zexiao Zhang Jie Zhang Jinyang Du Xiangdong Chen Wenjing Zhang Changmeng Peng |
| author_facet | Zexiao Zhang Jie Zhang Jinyang Du Xiangdong Chen Wenjing Zhang Changmeng Peng |
| author_sort | Zexiao Zhang |
| collection | DOAJ |
| description | In rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and environment limitations, which affects the detection performance. In view of the fact that pests and diseases affect the whole situation and tiny details are mostly localized, we propose a rice image reconstruction method based on an adaptive two-branch heterogeneous structure. The method consists of a low-frequency branch (LFB) that recovers global features using orientation-aware extended receptive fields to capture streaky global features, such as pests and diseases, and a high-frequency branch (HFB) that enhances detail edges through an adaptive enhancement mechanism to boost the clarity of local detail regions. By introducing the dynamic weight fusion mechanism (CSDW) and lightweight gating network (LFFN), the problem of the unbalanced fusion of frequency information for rice images in traditional methods is solved. Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. In downstream task validation, rice panicle maturity detection achieves a 61.5% increase in mAP50 (0.480 → 0.775) compared to interpolation methods, and leaf pest detection shows a 2.7% improvement in average mAP50 (0.949 → 0.975). This research provides an effective solution for lightweight rice image enhancement, with its dual-branch collaborative mechanism and dynamic fusion strategy establishing a new paradigm in agricultural rice image processing. |
| format | Article |
| id | doaj-art-3341206d02ad44cbae0e2b392b1708ea |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-3341206d02ad44cbae0e2b392b1708ea2025-08-20T02:48:17ZengMDPI AGAgronomy2073-43952025-07-01157172910.3390/agronomy15071729Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice ImageryZexiao Zhang0Jie Zhang1Jinyang Du2Xiangdong Chen3Wenjing Zhang4Changmeng Peng5School of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaSchool of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaSchool of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaSchool of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaSchool of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaSchool of Information Engineering, Sichuan Agricultural University, No. 46, Xinkang Road, Ya’an 625014, ChinaIn rice detection tasks, accurate identification of leaf streaks, pest and disease distribution, and spikelet hierarchies relies on high-quality images to distinguish between texture and hierarchy. However, existing images often suffer from texture blurring and contour shifting due to equipment and environment limitations, which affects the detection performance. In view of the fact that pests and diseases affect the whole situation and tiny details are mostly localized, we propose a rice image reconstruction method based on an adaptive two-branch heterogeneous structure. The method consists of a low-frequency branch (LFB) that recovers global features using orientation-aware extended receptive fields to capture streaky global features, such as pests and diseases, and a high-frequency branch (HFB) that enhances detail edges through an adaptive enhancement mechanism to boost the clarity of local detail regions. By introducing the dynamic weight fusion mechanism (CSDW) and lightweight gating network (LFFN), the problem of the unbalanced fusion of frequency information for rice images in traditional methods is solved. Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. In downstream task validation, rice panicle maturity detection achieves a 61.5% increase in mAP50 (0.480 → 0.775) compared to interpolation methods, and leaf pest detection shows a 2.7% improvement in average mAP50 (0.949 → 0.975). This research provides an effective solution for lightweight rice image enhancement, with its dual-branch collaborative mechanism and dynamic fusion strategy establishing a new paradigm in agricultural rice image processing.https://www.mdpi.com/2073-4395/15/7/1729agricultural image enhancementneural networksdual-branch structuredynamic fusioncrop detectiondisease identification |
| spellingShingle | Zexiao Zhang Jie Zhang Jinyang Du Xiangdong Chen Wenjing Zhang Changmeng Peng Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery Agronomy agricultural image enhancement neural networks dual-branch structure dynamic fusion crop detection disease identification |
| title | Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery |
| title_full | Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery |
| title_fullStr | Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery |
| title_full_unstemmed | Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery |
| title_short | Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery |
| title_sort | frequency domain collaborative lightweight super resolution for fine texture enhancement in rice imagery |
| topic | agricultural image enhancement neural networks dual-branch structure dynamic fusion crop detection disease identification |
| url | https://www.mdpi.com/2073-4395/15/7/1729 |
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