基于降噪技术的高光谱图像实时优化研究

Research on real-time optimization of hyperspectral images based onnoise reduction technology

为了获得理想的高光谱图像,针对当前高光谱图像优化方法存在的问题,设计了基于降噪技术的高光谱图像实时优化方法。首先分析当前高光谱图像优化的研究进展,找到当前方法存在的不足,采集高光谱图像,采用自适应阈值的小波变换对高光谱图像进行去噪处理,以改善高光谱图像质量,然后采用Retinex理论模型对去噪后高光谱图像进行增强操作,丰富高光谱图像细节信息,最后采用卷积神经网络进行了高光谱图像分类,测试结果表明:本方法优化后高光谱图像的峰值信噪比和结构相似度均值分别为31.18和0.981,不但提升了高光谱图像质量,而且使得高光谱图像分类正确率超过了92%,高光谱图像优化时间控制在4.5 s以内,相对于其他高光谱图像优化方法,具有十分明显的优越性。

In order to obtain ideal hyperspectral images,a real-time optimization method for hyperspectral images based on denoising technology was designed to address the problems existing in current optimization methods.Firstly,analyze the current research progress of hyperspectral image optimization,identify the shortcomings of current methods,collect hyperspectral images,use adaptive threshold wavelet transform to denoise hyperspectral images,improve the quality of hyperspectral images,and then use Retinex theoretical model to enhance the denoised hyperspectral images,enrich the details of hyperspectral images.Finally,use convolutional neural networks for hyperspectral image classification,the test results show that the peak signal-to-noise ratio and average structural similarity of hyperspectral images optimized by this method are 31.18 and 0.981,which improves the quality of hyperspectral images and makes the classification accuracy of hyperspectral images exceed 92%.The optimization time of hyperspectral images is controlled within 4.5 seconds,which has significant advantages compared to other hyperspectral image optimization methods.