An image enhancement algorithm based on unsupervised learning is proposed,aim at the problems of low illumination image enhancement algorithms,suffering from loss of recovery details,high network complexity,and difficulty obtaining paired data sets.In YIQ color space,the enhancement curve of luminance channel Y is calculated by the constructed lightweight network and power index function to get the image of the enhancement of the poorly exposed area and the containment of the high light area.The no-reference loss function used in this network can implicitly evaluate image enhancement quality and drive network learning.Experimental results show that the proposed algorithm achieves competitive results regarding visual effects and image quality when the trainable parameters and model weight only account for 9.5 k/88 kB.