Application of Blurred Image Processing and IoT Action Recognition in Sports Dance Sports Training

In order to process the blurred image, this study proposes to combine the blurred point functions in the invariant space into multiple blurred images and then restore them through the deconvolution operation. The PSF functions of the fuzzy invariant space are combined to obtain the fuzzy invariant space. Finally, a gradual restoration method is used to perform many blurred image processing steps. The experimental results prove that the method proposed in this study can avoid the noise introduced in the process of multiple deconvolutions, can reduce the calculation error, and can improve the recovery effect. Based on fuzzy image processing, this research studies the nature of human motion and the identification of actions in the Internet of Things, which provides new ideas and methods for recognition research. The Kinect somatosensory camera of the Internet of Things is used to capture deep images, and 20 three-dimensional points of the human skeleton structure are obtained through its SDK. Based on this, the motion characteristics of human joints were studied, and a motion resolution model suitable for the Internet of Things was proposed. The model has low complexity, simple calculation, and sorting characteristics. Based on this, this research study also uses software engineering ideas and general methods of system development to design and create sports dance management information systems and uses advanced methods such as computers and the Internet to maintain training management to achieve optimal sports training for sports dance mode and to provide information about the management of sports dance athletes training to improve efficiency and the management level.