Optical Flow Estimation and Denoising of Video Images Based on Deep Learning Models

In order to effectively extract image features highly related to visual perception quality, improve the image quality evaluation method, under the framework of deep learning, combining the optical flow method and the edge detection algorithm, a multi-feature fusion motion based on improved optical flow is proposed target detection algorithm. First, a video fluid model is proposed. The fluid model decomposes the video object area changes into sub-area zoom, rotation and translation movements. The rigid body area and the area hierarchy describe the spatial relationship of pixels, and the rigid body motion describes the time domain relationship of pixels. It provides a region-based video processing. The associated spatiotemporal is association method. Secondly, a video fluid model is proposed. The video fluid model treats all pixels of the same surface imaged in the video as a fluid, using streamlines to represent the regional motion of the object, and streamlines to represent the pixel motion of the video object, using rotators and translation lines to simplify the streamlines when necessary. The streamline of the same fluid is smooth in the time domain, and the flow pattern is smooth in both the time domain and the space domain. Finally, the top-down deep learning generation model conversion is carried out, and finally through continuous adjustment between different levels, the generation model can reconstruct the original sample with lower error, so that the essential characteristics of this sample are obtained, namely the highest abstract representation of the depth model. After processing the deep learning model, the sample features after dimensionality reduction can be obtained, and the recognition module is used on this basis. Experiments show that the optical flow estimation method based on deep learning and multi-grid, optical flow field estimation method based on variational model and desiccation method proposed in this paper are effective, and it is suitable for moving image analysis, target tracking and 3D reconstruction Such research has certain theoretical significance and practical application value.