面向显微视觉的端对端去模糊模型OA北大核心CSTPCD

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 multi-branch recovery was presented,in which a divide-and-conquer strategy of chunking,discrimination,de-blurring,and fusion was established.Firstly,the image was divided into sub-images,which were then si-multaneously processed by a discriminator and a recovery network.The discriminator employed the Fouri-er 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 blur-ring 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-im-ages were fused to obtain high-resolution images.The experimental results indicate that the model can ef-fectively restore multi-blurred microscopic images,with a discriminator accuracy reaching 0.94.More-over,after undergoing processing by the multi-branch restoration network,the PSNR metric shows an av-erage improvement of 6.3.