A contrast enhancement model based on the image retrieval is proposed to achieve the image contrast enhancement. We use the image retrieval technology to retrieve the high quality image as a reference image whose content is similar to the image to be enhanced to guide the enhancement of the image. The image entropy and aestheticis features between the image to be enhanced and the image retrieved are used to solve the multi-criteria optimization problems which combined the images via three enhancement methods such as the context-free, context-sensitive and brightness adjustment. Finally, the enhanced image is gotten by the quality constraint. In the context-sensitive enhancement, we use filter for unsharp masking to achieve edge enhancement. The context-free enhancement methods map transformations based on the Sigmod function to meet the human visual perception. The part of brightness adjustment firstly performs adaptive brightness equalization processing on the image, and then, a contrast limit adaptive histogram equalization transform is performed. Experimental results show that the proposed model can effectively adjust the contrast of the image and has better performance than other methods.