一种基于测试时训练的跨域图像去模糊方法OA

[Purpose]This study aims to address the problem of cross-domain image deblurring by propos-ing a novel test-time training method.[Methods]A defocus blur generation network(DBGN)is constructed by simulating the formation process of defocus blur,which is embedded at the end of the deblurring model to create an auxiliary task.During the training phase,the DBGN serves as an additional auxiliary loss to optimize the deblurring model and enhance deblurring accuracy.In the testing phase,the DBGN is utilized to perform a re-blurring task,acting as an auxiliary module to assist the primary deblurring model in updating parameters to adapt to out-of-distribution cross-domain data.[Results]The proposed method is tested on blurred images captured in inspection scenarios,validating its practical performance for cross-domain image deblurring in real-world settings.[Conclusions]Extensive experiments on multiple public defocus blur datasets and comparisons with current state-of-the-art methods demonstrate the effectiveness of the proposed approach.