Multi-person pose estimation is a challenging problem. Bottom-up methods have been greatly studied because the prediction speed of top-down methods is related to the number of people in the input image, making these methods difficult to apply in real-time environments. To solve the problems of scale sensitivity and quantization error in bottom-up methods, it is necessary to have a model that can predict multi-scale keypoints and refine quantization error. To achieve this, we propose context feature and refined network for multi-person (...) pose estimation (CRNet), which can effectively solve the problems of scale sensitivity and quantization error in bottom-up methods. We use a multi-scale feature pyramid and context feature to achieve scale invariance of the network. We extract global and local features and then fuse them by attentional feature fusion (AFF) to obtain context feature that adapt to multi-scale keypoints. In addition, we propose an efficient refined network to solve the problem of quantization error and use multi-resolution supervised learning to further improve the prediction accuracy of CRNet. Comprehensive experiments are conducted on two benchmarks: COCO and MPII datasets. The average precision of CRNet reached 72.1 and 80.2%, respectively, surpassing most state-of-the-art methods. (shrink)

Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is (...) processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements. (shrink)

A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy (...) of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application. (shrink)