Computed tomography (CT) images of large core samples acquired by imaging equipment are insufficiently clear and ineffectively describe the tiny pore structure; conversely, images of small core samples are insufficiently globally representative. To alleviate these challenges, the idea of a super-resolution reconstruction algorithm is combined with that of a three-dimensional core reconstruction algorithm, and a multi-scale core CT image fusion reconstruction algorithm is proposed. To obtain sufficient image quality with high resolution, a large-scale core image is used to provide global feature information as well as information regarding the basic morphological structure of a large-scale pore and particle. Then, the texture pattern and the tiny pore distribution information of a small-scale core image is used to refine the coarse large-scale core image. A blind image quality assessment is utilized to estimate the degradation model of core images at different scales. A multi-level pattern mapping dictionary containing local binary patterns is designed to speed up the pattern matching procedure, and an adaptive weighted reconstruction algorithm is designed to reduce the blockiness. With our method, images of the same core at different scales were successfully fused. The proposed algorithm is extensively tested on microstructures of different rock samples; all cases of the reconstructed results and those of the actual sample were found to be in good agreement with each other. The final reconstructed image contains both large-scale and small-scale information that can provide a better understanding of the core samples and inform the accurate calculation of parameters.

中文翻译：

---

核心图像的多尺度建模算法

通过成像设备获取的大型岩心样品的计算机断层扫描（CT）图像不够清晰，无法有效描述微小的孔结构；相反，小的核心样本的图像在全球范围内代表性不足。为了缓解这些挑战，将超分辨率重建算法的思想与三维核心重建算法的思想相结合，提出了一种多尺度核心CT图像融合重建算法。为了获得高分辨率的足够图像质量，使用大规模核心图像来提供全局特征信息以及有关大规模孔隙和粒子的基本形态结构的信息。然后，利用小尺度核心图像的纹理图案和微小孔隙分布信息对粗大尺度核心图像进行细化。盲图像质量评估用于估计不同比例的核心图像的退化模型。设计了一种包含局部二进制模式的多级模式映射字典，以加快模式匹配过程，并设计一种自适应加权重建算法，以减少块效应。使用我们的方法，成功融合了不同比例下相同核心的图像。该算法在不同岩石样品的微观结构上进行了广泛的测试。重建结果的所有案例与实际样本的案例均相互一致。