An Improved Visual Recognition Model of Interference Noise Image

Edge computing can take advantage of the convenience of a relatively close test environment to quickly obtain on-site environmental data. Image data collection can be quickly acquired and can be repeatedly learned and analyzed in big data. In this study, establish an edge computing inspection mechanism, through structural similarity index measure (SSIM) image recognition, first compare the sampling target data to analyze whether it is the same as the previous sampling. Because of the difference in image value changes after flickering during the image sampling process, when the digital image comparison is completed, the image data are sampled and compared through the hierarchical distribution verification technology, and then analyzed. The image visual comparison model proposed in the research can improve the image comparison accuracy by 4%∼5% and optimize the calculation efficiency through experimental verification.