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The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points. [ABSTRACT FROM AUTHOR]

A panoramic image texture detail enhancement method based on the Retinex algorithm is proposed to work on the nature of panoramic images. Firstly, the panoramic image is collected, then the panoramic image is preprocessed through brightness enhancement, the brightness of the preprocessed panoramic image is normalized, and the panoramic image is optimized and increased by the improved Retinex algorithm. Finally, the simulation test of the panoramic image was carried out. The results show that the improved Retinex algorithm works on the sign to-commotion proportion of the panoramic image. Furthermore, the time required to enhance the panoramic texture detail is short, which can meet the practical requirements of panoramic image subsequent processing. [ABSTRACT FROM AUTHOR]