Multi-Spectral Fusion and Denoising of RGB and NIR Images Using Multi-Scale Wavelet Analysis

In this paper, we propose multi-spectral fusion and denoising (MFD) of RGB and NIR images using multi-scale wavelet analysis. We formulate MFD of RGB and NIR images as a maximum a posterior (MAP) estimation problem in the wavelet domain. The direct fusion of noisy RGB and NIR image often leads to contrast attenuation due to the discrepancy between RGB and NIR images. Thus, we generate the wavelet scale map for fusion and denoising based on correlation between NIR and RGB wavelet coefficients. To consider local contrast and visibility of NIR data on RGB components, we provide the contrast preservation term for scale map estimation based on the local contrast and visibility. We use the regularization term to select high visibility and contrast of NIR wavelet coefficients in the scale map. Since noise generally appears in the high frequency band, we use gradients of NIR wavelet coefficients as the weight for weighted least square (WLS) smoothing in the scale map. Based on the wavlet scale map, we perform fusion and denoising of RGB and NIR wavelet coefficients. Experimental results show that the proposed method successfully performs fusion of RGB and NIR images with noise reduction and detail preservation as well as outperforms state-of-the-arts in terms of discrete entropy (DE) and feature-based blind image quality evaluator (FBIQE).