IEEE J Biomed Health Inform. 2022 Nov;26(11):5584-5595. doi: 10.1109/JBHI.2022.3196710. Epub 2022 Nov 10.
Medical image fusion technology integrates the contents of medical images of different modalities, thereby assisting users of medical images to better understand their meaning. However, the fusion of medical images corrupted by noise remains a challenge. To solve the existing problems in medical image fusion and denoising algorithms related to excessive blur, unclean denoising, gradient information loss, and color distortion, a novel medical image fusion and denoising algorithm is proposed. First, a new image layer decomposition model based on hybrid variation-sparse representation and weighted Schatten p-norm is proposed. The alternating direction method of multipliers is used to update the structure, detail layer dictionary, and detail layer coefficient map of the input image while denoising. Subsequently, appropriate fusion rules are employed for the structure layers and detail layer coefficient maps. Finally, the fused image is restored using the fused structure layer, detail layer dictionary, and detail layer coefficient maps. A large number of experiments confirm the superiority of the proposed algorithm over other algorithms. The proposed medical image fusion and denoising algorithm can effectively remove noise while retaining the gradient information without color distortion.
医学图像融合技术整合了不同模式的医学图像内容，从而帮助医学图像用户更好地理解其含义。然而，噪声污染的医学图像的融合仍然是一个挑战。为了解决医学图像融合和去噪算法中存在的过度模糊、去噪不干净、梯度信息丢失和颜色失真的问题，提出了一种新的医学图像融合和去噪算法。首先，提出了一种基于混合变分稀疏表示和加权 Schatten p-范数的新的图像层分解模型。使用交替方向乘子法更新输入图像的结构、细节层字典和细节层系数图，同时进行去噪。然后，对结构层和细节层系数图采用适当的融合规则。最后，使用融合的结构层、细节层字典和细节层系数图来恢复融合图像。大量实验证实了该算法优于其他算法。所提出的医学图像融合和去噪算法可以在不产生颜色失真的情况下有效去除噪声并保留梯度信息。
