Image quality control method and device based on lightweight convolutional neural network, and medium
The invention discloses an image quality control method and device based on a lightweight convolutional neural network, and a medium, and is applied to the technical field of image analysis. The method comprises the following steps: firstly, acquiring a to-be-detected drug sensitive holographic imag...
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| creator | SUN QIANPENG HOU JIANPING WANG CHI ZHAO WANLI LIU CONG WANG CHAO ZHANG QIANQIAN |
| description | The invention discloses an image quality control method and device based on a lightweight convolutional neural network, and a medium, and is applied to the technical field of image analysis. The method comprises the following steps: firstly, acquiring a to-be-detected drug sensitive holographic image; and inputting the drug sensitivity holographic image into a drug sensitivity holographic image quality judgment model. And finally, determining the quality of the drug sensitivity holographic image according to an output result of the drug sensitivity holographic image quality judgment model. According to the drug sensitivity holographic image quality judgment model, the feature maps of different scales can be fused to obtain the final target feature map, high recognition accuracy can be obtained under the condition that the number of layers of the neural network is small, and due to the fact that the number of layers of the neural network is small, the calculation time is shortened, and the image quality recogn |
| format | Patent |
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The method comprises the following steps: firstly, acquiring a to-be-detected drug sensitive holographic image; and inputting the drug sensitivity holographic image into a drug sensitivity holographic image quality judgment model. And finally, determining the quality of the drug sensitivity holographic image according to an output result of the drug sensitivity holographic image quality judgment model. According to the drug sensitivity holographic image quality judgment model, the feature maps of different scales can be fused to obtain the final target feature map, high recognition accuracy can be obtained under the condition that the number of layers of the neural network is small, and due to the fact that the number of layers of the neural network is small, the calculation time is shortened, and the image quality recogn</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Image quality control method and device based on lightweight convolutional neural network, and medium |
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