Near-infrared Image Guided Neural Networks for Color Image Denoising

Noisy color image and guided near-infrared (NIR) image can be jointly employed to eliminate noise and enhance details. Existing methods mostly rely on explicit designed filters and hand-crafted objective function optimization. These methods usually introduce erroneous structures from guidance signal. Besides, they are time-consuming and not suitable for real time applications. In this paper, we come up with a learning based method. The noisy color image and NIR image are fused, then fed into a fully convolutional neural network. The network learns a directly map from degraded image to restored sharp image. Our architecture can effectively eliminate image noise and transfer detail structure from guided image. Our trained network accepts any resolution of input image and runs in constant time. We evaluate the presented approach on both synthetic and real images. Results show that our approach outperforms the state-of-art methods.