Video Stabilization Method Based on Recurrent Neural Network Iterative Strategy

Abstract

The invention discloses a video stabilization method based on a cyclic neural network iterative strategy. The method includes: capturing paired video data with a shaking video collection and stabilization processing hardware device; preprocessing the collected video stream samples; designing and constructing End-to-end fully convolutional deep neural network based on the intra-frame and inter-frame iteration strategy of the cyclic neural network; input the preprocessed training data into the cyclic neural network, and use the linear weighting of four losses to guide the training process of the network parameters, Get the trained model; input the low-quality jitter test video into the trained neural network to get a stable version of the target video. The present invention transmits historical motion state information for each current video frame in time series through the iterative strategy of the cyclic neural network, which enhances the network's ability to perceive the shaking sequence frame information, thereby more accurately predicting stable pictures.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a video stabilization method based on an iterative strategy of a cyclic neural network. Background technique [0002] Remote sensing hyperspectral image super-resolution is a widely used and popular research field. Video is a time-series combination expression of images. Many video processing algorithms are not robust to some low-quality videos (blurry, noise, picture jitter, insufficient light), so video quality is the key to testing the performance of video processing algorithms. . The video image stabilization can be used as a preprocessing step of these algorithms to further improve the algorithm performance by improving the video picture quality. The stabilized video can be better applied to various visual tasks such as super-resolution and classification. [0003] The traditional mainstream video image stabilization method is an image processing m...

AI Technical Summary

Problems solved by technology

However, these deep video stabilization methods only stack adjacent temporal video frames in the input channel dimension, and then design a temporal regularization term to allow the convolutional network to learn the coherence of motion between frames, but this method cannot be well utilized. Timing information of adjacent frames

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