Multi-scale Retinex Image Enhancement Algorithm Based on Fabric Defect Database

In order to meet the demands of fabric defect detection under different lighting conditions, the multi-scale Retinex algorithm is proposed as preprocessing algorithm to limit the influence of lighting change on subsequent processing to a certain degree. Firstly, the fabric defect simulation database under complex lighting conditions is produced by rotating, flipping and transforming the data based on traditional TILDA fabric texture database. Aiming at the phenomenon of the obvious brightness changes between different images in the database and the more complicated illumination environment, the multi-scale Retinex algorithm as the preprocessing is used by logarithmically transforming the given input image and estimating the incident image in this paper. The input image and the estimated incident image are reflected images, which limits the influence of illumination changes on subsequent processing to a certain extent. The comparative experiments show that dynamic range compression, color constancy, edge enhancement and a balance between the three aspects can be achieved by the multi-scale Retinex algorithm at the same time. The experimental results show that the multi-scale Retinex algorithm is robust, and the local details of the processed image will be well maintained. The image information entropy and contrast is increased by 30%, and average gradient is increased by nearly 40%. Simultaneously, the change of light and noise will be limited to a certain degree, and high-quality fabric image under different illumination conditions can be obtained effectively.