Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Abstract

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.

A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation

“…An alternating direction method of multiplier was applied to optimization of the simultaneous image fusion and denoising. Under the assumption of both RGB and near infrared (NIR) images containing the same well-calibrated spatial resolution, multi-scale wavelet analysis was integrated into a multi-spectral fusion and denoising framework to achieve texture transfer and noise removal [42]. A discrepancy model based on the wavelet scale map was used to solve the discrepancy between RGB and NIR images.…”

Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods

“…In contrast, the near-infrared (NIR) channel has a high reflectance and generally absorbs 10% or less of the radiation [20]. Compared with RGB images under lowillumination conditions, NIR images have a clear structure and no noise impairment [21,22]. Therefore, multispectral images with near-infrared (NIR) bands can be used more effectively for crop segmentation and counting.…”

Enhancing infrared color reproducibility through multispectral image processing using RGB and three infrared channels

“…[1][2][3][4] Another approach involves combining VIS and infrared (IR) light by obtaining luminance information from the sensor response of IR and synthesizing color information from the sensor response of VIS light so that color image representation can be used, even in dim environments. [5][6][7][8][9][10][11][12][13] However, neither of these methods can be used to obtain color information in the complete absence of VIS light. To solve this problem, the usual approach is color estimation of monochrome images.…”