This study proposed an image denoising algorithm based on deep learning. The scale-perception edge-protection filter was used to decompose the noise image in multiple scales. Small features, such as the image noise, were removed via scale sensing and edge preserving, and the edge details were kept unchanged. A trained convolutional neural network model was used to gather detailed information about the image, and the image was then processed using the scale-perception edge-protection filter for detail recovery. The results show that the proposed denoising algorithm can effectively reduce noises and well retain high-frequency information. Moreover, the fusion results correlate well with human visual observations.