Neural network-based motion vector estimation algorithm for dynamic image sequences

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With the rapid development of deep learning, convolutional neural networks have gradually become the main means to extract features of dynamic image sequences. The motion vector estimation algorithm, as the key to the stability of image sequences, directly affects the performance of image stabilization systems, so the motion estimation algorithm for convolutional neural networks is necessary. The study proposes an improved convolutional neural network based on loss-free function, and applies it to the extraction of dynamic image features. On this basis, the motion estimation algorithm is then optimised by combining grey-scale projection and block matching methods. The experimental results show that the new loss-free function-based convolutional neural network has better recognition capability with an error rate of only 15% in dynamic image recognition. The accuracy of the optimised motion estimation algorithm is as high as 95.1% with a PSNR value of 16.636, which is higher than that of the traditional grey-scale projection algorithm. In terms of video processing, the improved algorithm has a higher PSNR value than the search block matching method, the bit-plane matching method and the full search block matching method, with a higher steady image accuracy and high operational efficiency, providing a new research idea for the improvement of motion estimation algorithms. In general, the proposed algorithm is a significant improvement over the current mainstream algorithms in terms of image accuracy, processing performance and number of operations, and it provides a new research idea for the improvement of motion estimation algorithms.