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Significant advancements have been made in image deblurring through multi-layer networks, but their performance remains limited by challenges in feature extraction and residual connections. To address these issues, this paper proposes a multi-scale feature extraction and fusion network (MSFN) for image deblurring. The core idea of the network is to enhance image feature extraction through multi-scale inputs and outputs. Further, MSFN utilizes its feature adaptive detail enhancement (ADE) modules and cross-scale feature fusion (CSFF) modules to capture multi-scale features at different network depths, thereby optimizing the residual connection process and effectively integrating multi-scale information. Experimental results demonstrate that the proposed algorithm achieves superiority in quantitative analysis and significantly improves subjective visual effects, exhibiting an advanced performance.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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