Unsupervised video reconstruction based on event camera
In practical applications,the difference between event clock and traditional camera frame rate often leads to a certain spastiotemporal difference between the two,and it is difficult to obtain an accurate one-to-one corresponding event-image data pair,resulting in the failure of effective supervision and network training.Aiming at the difficulty of spatiotemporal matching,an unsupervised video reconstruction method based on event camera is proposed by using the characteristics of the overall distribution of images learned in the cyclic generative adversarial networks,which realizes unsupervised reconstruction of non-spatiotemporal matching based on event camera.The experimental results show that compared with the existing video reconstruction methods based on event camera,the proposed method has improved in three indicators:structural similarity(SSIM),mean square error(MSE)and blind/referenceless image spatial quality evaluator(BRISQUE).In the absence of spatiotemporal matching data,the proposed method can reconstruct a relatively clear and high frame rate video.
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