Decomposition method of image texture and structure based on a total variational model of a guide map

Abstract

The invention discloses a decomposition method of image texture and structure based on a total variational model of a guide map, belonging to the technical field of image processing. According to theinvention, firstly, the local main structure of the image is reconstructed based on the guide image filter, then the texture descriptor is calculated according to the reconstructed local structure image, finally, the accuracy and computational efficiency of texture and structure decomposition are improved by combining the multi-scale total variational model and block translation method. The technical scheme of the invention can obtain better texture/structure decomposition effect for noisy images, the textures and structures of different scales can also be decomposed accurately, and the decomposed structure layers can keep the original shading of the image, avoiding the blurring of the structure caused by local smoothing or the color block effect caused by the global optimization method. In addition, the technical proposal of the invention needs to extract simple features, and does not depend on learning a large number of image samples.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for decomposing image texture and structure based on a guide graph full variation model. Background technique [0002] Texture and structure are the most basic visual features inherent in images, and separating texture and structure in images is of great significance for further processing and understanding images. Accurately and efficiently separating texture and structure in complex scene images is a challenging task. In practical applications, filtering-based methods and total variation-based methods are two commonly used texture structure decomposition methods. [0003] The filter-based method completes the decomposition by calculating the difference between the local texture and structural features of the image. Its advantage is that the method is simple, intuitive, and fast. The defect is that it is difficult to accurately reconstruct weak edg...

AI Technical Summary

Problems solved by technology

However, the texture and structure decomposition based on total variation is very sensitive to the texture description method, and the selection of hyperparameters is also very difficult. Global optimization can easily lead to excessive smoothing of the decomposed structure image, lack of reasonable local light and shade changes, and degrade in some areas. for a single color block

In addition, decomposing texture and structure based on the total variational model is computationally intensive, and it is not suitable for applications that have strict requirements on computing time

Images