高光谱图像(Hyperspectral Images, HSIs)在遥感和医学成像等领域具有广泛的应用,但在采集过程中容易受到各种噪声的干扰。尽管目前已有多种去噪方法应用于高光谱图像处理,但这些方法在应对复杂光谱特征和复杂或非均匀噪声分布时仍面临挑战,且部分方法可能导致图像细节的丢失,降低光谱数据的真实性和有效性。为克服这些不足,文章提出了一种基于多元高斯混合模型的高光谱图像去噪方法。该方法将噪声建模为多元高斯混合模型,通过多模态特征表示图像的复杂光谱结构,以更好地适应不同光谱分布。我们采用变分贝叶斯(Variational Bayes, VB)方法进行参数估计,从而改善了传统期望最大化(EM)算法易于陷入局部最优的局限性,提高了参数估计的稳定性和模型收敛效率。实验结果表明,本文方法在多个数据集和多种噪声情况下均表现出优异的去噪效果,还能更好地保持光谱特性和图像结构的一致性,验证了其在高光谱图像去噪任务中的有效性。Hyperspectral Images (HSIs) have widespread applications in fields such as remote sensing and medical imaging, but they are often subject to various types of noise during the acquisition process. Despite the availability of numerous denoising methods for HSI processing, these methods still face challenges in handling complex spectral features and non-uniform noise distributions, which may lead to the loss of image details and compromise the authenticity and effectiveness of spectral data. To address these shortcomings, this paper proposes a hyperspectral image denoising method based on a multivariate Gaussian mixture model. The proposed method models noise using a multivariate Gaussian mixture model, employing multimodal features to represent the complex spectral structure of images, thereby better adapting to diverse spectral distributions. We adopt the Variational Bayes (VB) method for parameter estimation to overcome the limit
近年来深度卷积神经网络在图像去噪中的应用引起了越来越多的研究兴趣。然而,对于复杂的任务,如真实的噪声图像,普通网络无法恢复精细的细节。提出了一种经过注意力机制引导的双重去噪网络来恢复干净的图像。具体来说,该网络由四个模块组成,扩张特征提取块Dilated Feature Extraction Block (DFEB)、动态卷积块Dynamic convolution structure diagram、注意力模块,重建模块。具有稀疏机制的特征提取模块经由两个子网络提取全局和局部特征。增强块收集并融合全局和局部特征,为后者的网络提供补充信息。压缩块细化所提取的信息并压缩网络。最后,利用重建区块重建去噪影像。该网络具有以下优点:1) 双网络结构具有稀疏机制,可以提取不同的特征,增强去噪器的泛化能力。2) 融合全局和局部特征可以提取显著特征,从而恢复复杂噪声图像的细节。大量的实验结果表明,该网络有较好的去噪效果。In recent years, the application of deep convolutional neural networks in image denoising has attracted more and more research interest. However, for complex tasks, such as real noisy images, ordinary networks cannot recover fine details. A dual denoising network guided by attention mechanism is proposed to restore clean images. Specifically, the network consists of four modules: Dilated Feature Extraction Block (DFEB), Dynamic convolution structure diagram, attention module and reconstruction module. Feature extraction blocks with sparse mechanism extract global and local features through two subnetworks. Enhancement blocks collect and fuse global and local features to provide supplementary information to the latter’s network. The compressed block refines the extracted information and compresses the network. Finally, the reconstructed block is used to reconstruct the denoised image. The network has the following advantages: 1) the dual network structure has a sparse mechanism, which can extrac
