【摘要】 显微视觉测量在微装配等领域中应用广泛，受成像景深等因素的影响，图像会出现多重离焦模糊现象，影响后续准确测量，而显微自动对焦技术虽然可以缓解离焦问题，但耗时较长，难以适应高效生产要求。本文提出了将模糊度判别和多分支恢复相结合的端对端去模糊模型，建立了分块、判别、去模糊、融合的分而治之策略：首先将一幅图像切割成子图像组，同时送入判别器和恢复网络；在判别器中，通过傅里叶变换等获取频域分布，再利用Vision-Transformer网络从频域图中提取具有全局关联性的频域深层模糊特征，然后对模糊度进行判别输出。根据判别结果，由多分支恢复网络对不同模糊度的子图像进行定向恢复，最后融合拼接处理后的子图像，获得高清晰度的图像。实验结果表明，本文提出的模型能有效恢复多重模糊的显微图像，判别准确率达0.94，而模糊图像经过多分支恢复网络处理后，PSNR指标平均提升了6.3。更多还原

【Abstract】 The measurement of microscopic vision is commonly used in micro-assembly and other fields.However, due to limitations such as depth of field in microscopic imaging, the image may appear blurred and affect the accuracy of measurement. Although the technology of auto-focusing in optical microscopy can alleviate defocusing problems, it will be too time-consuming to adapt to the requirements of efficient production. Herein, an end-to-end deblurring model that integrates blurring discrimination and multibranch recovery was presented, in which a divide-and-conquer strategy of chunking, discrimination, deblurring, and fusion was established. Firstly, the image was divided into sub-images, which were then simultaneously processed by a discriminator and a recovery network. The discriminator employed the Fourier transform to obtain the frequency-domain map of the sub-images. From the frequency domain map, the Vision Transformer network extracted deep blur features with global correlation. The output of the blurring degree was then discriminated. The multi-branch recovery network was used to directionally recover sub-images with different blurring degrees based on the discriminative output. Finally, the spliced sub-images were fused to obtain high-resolution images. The experimental results indicate that the model can effectively restore multi-blurred microscopic images, with a discriminator accuracy reaching 0. 94. Moreover, after undergoing processing by the multi-branch restoration network, the PSNR metric shows an average improvement of 6. 3.更多还原