School of Electronic and Engineering, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi 710071, China.
Sensors (Basel). 2021 May 22;21(11):3610. doi: 10.3390/s21113610.
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.
我们将亮度通道的多光谱融合和去噪问题表述为小波域中的最大后验概率估计问题。为了解决融合中 RGB 和近红外（NIR）数据之间的差异，我们构建了一个差异模型并引入了小波尺度图。尺度图调整 NIR 数据的小波系数，使其具有与 RGB 数据相同的分布。我们分别使用小波尺度图及其梯度的先验作为对比度保持项和梯度去噪项。具体来说，我们在对比度保持项中利用局部对比度和可见度测量来将选定的 NIR 数据传输到融合结果中。我们还使用 NIR 小波系数的梯度作为小波尺度图中梯度去噪项的权重。基于小波尺度图，我们在基层和细节层中对 RGB 和 NIR 小波系数进行融合。为了去除噪声，我们使用 NIR 引导的拉普拉斯分布来对融合后的小波系数进行建模。在色度通道中，我们使用融合后的亮度通道来去除噪声。基于融合后的亮度变化，我们进一步增强了融合图像的颜色。实验结果表明，所提出的方法成功地实现了 RGB 和 NIR 图像的融合，同时实现了降噪、细节保留和颜色增强。
