A convenient solution to RGB-Infrared photography is to extend the basic RGB mosaic with a fourth filter type with high transmittance in the near-infrared band. Unfortunately, applying conventional demosaicing algorithms to RGB-IR sensors is not possible for two reasons. First, the RGB and near-infrared image are differently focused due to different refractive indices of each band. Second, manufacturing constraints introduce crosstalk between RGB and IR channels. In this paper we propose a novel image formation model for RGB-IR cameras that can be easily calibrated, and propose an efficient algorithm that jointly addresses three restoration problems--channel deblurring, channel separation and pixel demosaicing--using quadratic image regularizers. We also extend our algorithm to handle more general regularizers and pixel saturation. Experiments show that our method produces sharp, full-resolution images of pure RGB color and IR.

Color, infrared, and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images in different fields, for example, one noisy color image and one dark-flashed near infrared image. The major issue in such a framework is to handle structure divergence and find commonly usable edges and smooth transition for visually compelling image reconstruction. We introduce a scale map as a competent representation to explicitly model derivative-level confidence and propose new functions and a numerical solver to effectively infer it following new structural observations. Our method is general and shows a principled way for cross-field restoration.

Near-infrared (NIR) light has stronger penetration capability than visible light due to its long wavelengths and is thus less scattered by particles in the air. This makes it desirable for image dehazing to unveil details of distant objects in landscape photographs. In this paper, we propose an improved image dehazing scheme using a pair of color and NIR images, which effectively estimates the airlight color and transfers details from the NIR. A two-stage dehazing method is proposed by exploiting the dissimilarity between RGB and NIR for airlight color estimation, followed by a dehazing procedure through an optimization framework. Experiments on captured haze images show that our method can achieve substantial improvements on the detail recovery and the color distribution over the existing image dehazing algorithms.