In consideration of the problems of low utilization rate of action information and insufficient attention of temporal information in video human action recognition, a human action recognition model based on tightly coupled spatiotemporal two-stream convolutional neural network was proposed. Firstly, two 2D convolutional neural networks were used to separately extract the spatial and temporal features in the video. Then, the forget gate module in the Long Short-Term Memory (LSTM) network was used to establish the feature-level tightly coupled connections between different sampled segments to achieve the transfer of information flow. After that, the Bi-directional Long Short-Term Memory (Bi-LSTM) network was used to evaluate the importance of each sampled segment and assign adaptive weight to it. Finally, the spatiotemporal two-stream features were combined to complete the human action recognition. The accuracy rates of this model on the datasets UCF101 and HMDB51 selected for the experiment and verification were 94.2% and 70.1% respectively. Experimental results show that the proposed model can effectively improve the utilization rate of temporal information and the ability of overall action representation, thus significantly improving the accuracy of human action recognition.

To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.