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VIDEO BLIND DENOISING METHOD BASED ON DEEP LEARNING, COMPUTER DEVICE AND COMPUTER-READABLE STORAGE MEDIUM

机译:基于深度学习,计算机设备和计算机可读存储介质的视频盲人去噪方法

摘要

A video blind denoising method based on deep learning, a computer device and a computer-readable storage medium. The method includes: taking a video sequence from a video to be denoised, taking the middle frame in the video sequence as a noisy reference frame, performing an optical flow estimation on the image corresponding to the noisy reference frame and each other frame in the video sequence, to obtain optical flow fields; transforming, according to the optical flow fields, the image corresponding to each other frame in the video sequence to the noisy reference frame for registration respectively, to obtain multi-frame noisy registration images; taking the multi-frame noisy registration images as an input of a convolutional neural network, taking the noisy reference frame as the reference image, performing iterative training and denoising by using the noise2noise training principle, to obtain the denoised image. This solution may achieve the blind denoising of a video.
机译:基于深度学习,计算机设备和计算机可读存储介质的视频盲人去噪方法。 该方法包括:从视频序列中以噪声序列中的中间帧以噪声参考帧中的中间帧,对应于噪声参考帧的图像和视频中的彼此相对应的图像中的光流程估计 序列,获得光学流场; 根据光学流场转换,图像在视频序列中的彼此对应的图像分别进行嘈杂的参考帧,得到多帧噪声的登记图像; 将多帧嘈杂的登记图像作为卷积神经网络的输入,采用嘈杂的参考帧作为参考图像,通过使用噪声2noise训练原理来执行迭代训练和去噪,获得去噪图像。 该解决方案可以实现视频的盲目去噪。

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