RGB-NIR image enhancement by fusing bilateral and weighted least squares filters

Abstract

Image enhancement using visible (RGB) and near-infrared (NIR) image data has been shown to enhance useful details of the image. While the enhanced images are commonly evaluated by observers’ perception, in the present work, we rather evaluate it by quantitative feature evaluation. The proposed algorithm presents a new method to enhance the visible images using NIR information via edge-preserving filters, and also investigates which method performs best from an image features standpoint. In this work, we combine two edge-preserving filters: bilateral filter (BF) and weighted least squares optimization framework (WLS). To fuse the RGB and NIR images, we obtain the base and detail images for both filters. The NIR-detail images for both filters are simply fused by taking an average/maximum of both, which is then combined with the RGB-base image from the WLS filter to reconstruct the final enhanced RGB-NIR image. We then show that our proposed enhancement method produces more stable features than the existing state-of-the-art methods on RGB-NIR Scene Dataset. For feature matching, we use the SIFT features. As a use case, the proposed fusion method is tested on two challenging biometric verifications tasks using CMU hyperspectral face and CASIA multispectral palmprint databases. Our exhaustive experiments show that the proposed fusion method performs equally well in comparison to the existing biometric fusion methods.