Image denoising is a challenging research problem which aims to recover noise-free images from those which are contaminated with noise. In this paper, we focus on the denoising of images contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model which uses the output of three image denoising models namely, ADNet, DnCNN, and IRCNN in the ratio of 2:3:6 respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the Convolutional layers. For the third model which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.