The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method. However, the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information. So we propose a rendered image denoising method with filtering guided by lighting in-formation. First, we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas. Then, we establish the parameter prediction model guided by lighting information for fil-tering (PGLF) to predict the filtering parameters of different illumination areas. For different illumination areas, we use these filtering parameters to construct area filters, the filters are guided by the lighting information to perform sub-area filtering. Finally the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image. Under the PBRT scene and Tungsten dataset, the experimental results show that compared with other guided filtering denoising methods, our method improves the Peak Signal-to-Noise Ratio (PSNR) metrics by 4.2164DB on average and the Structural Similarity Index (SSIM) metrics by 7.8% on average. This shows that our method can better reduce the noise in complex lighting scenes and improve the image quality.