KEYWORDS: Image fusion, Multispectral imaging, RGB color model, Color imaging, Image quality, Light sources and illumination, Color difference, Signal to noise ratio, Feature extraction
In low light conditions, color (RGB) images taken by cameras contain a lot of noise and loss of detail and color. However, multispectral images can provide spectral information, which can be fused by neural network models . In this paper, an improved U-Net model is proposed for multi-spectral image fusion to achieve color imaging in low illumination environment. The U-Net model is a symmetric convolutional neural network that helps extract and combine features at various image scales. Our improved U-Net model integrates residual blocks and attention mechanisms, utilizing multilevel feature extraction and contextual information fusion to significantly enhance imaging quality. To meet the requirements of low-light conditions, the model design incorporates a multi-scale feature fusion strategy, bolstering robustness against weak light and noise. We conducted multiple experiments at different light levels to validate the effectiveness of the model. The quality of the fused color images was evaluated with objective assessment metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and chromatic aberration (ΔE). The experimental results demonstrated the effectiveness of the proposed method, which can generate color images with high color reproduction and rich detail under low light conditions. Compared to traditional methods, our approach shows substantial improvements in image clarity, noise suppression, and color authenticity, indicating significant practical value. In summary, this study combines deep learning with multispectral image fusion to propose an effective method for low-light color imaging. It offers new insights and technical solutions for addressing low-light imaging challenges in practical applications.
In this paper, the laser confocal Raman spectrum of glucose standard solution was measured and studied. The relative intensity of the characteristic peak of glucose and the characteristic peak of water in the solution was used as the ratio of the measured content to measure the concentration of glucose solution.Put the prepared glucose solutions of different concentrations in a cuvette, place them on a glass slide, and place them under the field of view of the objective lens of the laser microscope Raman spectrometer stage, adjust the focal length through the eyepiece, focus the laser on the surface of the solution, and then measure Raman spectra of different solutions.The experimental results show that the relative intensity of the 1125cm-1 peak has the same trend as the actual value of glucose concentration, and there is a good linear relationship between them. This linear correlation reaches more than 0.93, and the measured LOD value is 160mg/dL This result confirms that the glucose concentration detected by Raman spectroscopy using this analytical method can be close to the normal value of human blood sugar. This will help to explore the relationship between the ratio of blood glucose peak to hemoglobin peak intensity and blood glucose content, and lay a good foundation for noninvasive blood glucose research.
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