We proposed a composite image guided filtering technique for dynamic PET denoising to enable quantitatively enhanced time frames. The guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or a different image. In this paper, the composite image from the entire time series is considered as the guidance image. Thus, a local linear model is established between the composite image and individual PET time frames. Subsequently, linear ridge regression is exploited to derive an explicit composite image guided filter. For validation, 20 minute FDG PET data from a NEMA NU 4-2008 IQ phantom were acquired in the list-mode format via the Siemens Invoen micro PET, and were subsequently divided and reconstructed into 20 frames. We compared the performances (including visual and quantitative profiles) of the proposed composite image guide filter (CIGF) with a classic Gaussian filter (GF), and a highly constrained back projection (HYPR) filter. The experimental results demonstrated the proposed filter to achieve superior visual and quantitative performance without sacrificing spatial resolution. The proposed CIGF is considerably effective and has great potential to process the data with high noise for dynamic PET scans.