Recognition of lawn weeds based on retinex algorithm with local density fusion

Abstract

In order to solve the problem of difficulty in segmenting the foreground of lawn weed images due to the similarity between the foreground and background grayscale, this paper proposes a Retinex enhancement algorithm based on local density fusion. Firstly, image preprocessing is completed based on local variance to realize prominent image foreground and smooth cluttered background; Secondly, using multi threshold segmentation and operational differentiation, pixels are divided into three categories: foreground, background, and pixels to be subdivided. Local density is used to extract spatial information of pixels to be subdivided; Finally, the sigmoid function is used to optimize the reflection component grayscale transformation coefficient, achieving the fusion of local density information and obtaining the final image. The results show that the algorithm proposed in this paper expands the gray difference between weeds and lawn grass while suppressing background noise; Compared with other enhancement algorithms, the peak signal-to-noise ratio, average running time, and segmentation accuracy have all been improved greatly. At the same time, through image analysis, it has been found that the algorithm results in occasional problems of uneven brightness and over segmentation of fine leaf weeds in the image. In response to these two problems, this article finally proposes two corresponding optimization measures, namely global brightness adjustment based on a new adaptive gamma function and Retinex reflection component saturation adjustment based on AHE.