This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms.