Multispectral Fusion Of Rgb And Nir Images Using Weighted Least Squares And Alternating Guidance

In low light condition, color (RGB) images captured by camera contain much noise and loss of details and color. However, near infrared (NIR) images are robust to noise and have clear textures without color. In this paper, we propose multispectral fusion of RGB and NIR images using weighted least squares (WLS) and alternating guidance. Low light RGB images provide coarse image structure and color, while NIR images offer clear textures in a short distance. Since they are complementary, we adopt alternating guidance for fusion of RGB and NIR images based on WLS. First, we perform the first guidance for denoising on noisy RGB image to get base layer. Then, we conduct the second guidance for texture transfer on NIR image to get detail layer. Finally, we combine base and detail layers to produce a fusion result. We maximize the advantage of multispectral input (RGB and NIR) for fusion based on alternating guidance. Experimental results demonstrate that the proposed method achieves good performance in noise reduction, detail preservation and color reproduction as well as outperforms state-of-the-art ones in terms of quantitative measurement and computational efficiency.