Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction
Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction
复制标题
DOI：
10.1109/tmm.2019.2958760
复制
发表时间：
2018-12
影响因子：
7.3
通讯作者：
Qiqin Dai;Henry H. Chopp;E. Pouyet;O. Cossairt;M. Walton;A. Katsaggelos
中科院分区：
文献类型：
--
作者：
Qiqin Dai;Henry H. Chopp;E. Pouyet;O. Cossairt;M. Walton;A. Katsaggelos
提出了一种基于深度学习的自适应图像采样算法。它由自适应采样模板生成网络和图像修复网络联合训练而成。采样率由掩码生成网络控制，研究了一种二值化策略使采样掩码二值化。除了图像采样和重建过程外，我们还展示了如何对其进行扩展，并将其用于加速栅格扫描，例如X射线荧光(XRF)图像扫描过程。最近，由于X射线产生和检测方面的技术进步，基于XRF实验室的系统已经演变为轻便和便携的仪器。然而，XRF图像的扫描时间通常很长，因为需要长时间曝光(例如，每点${\Text{100}}\；\MU{\Text{S}}-{\Text{1 ms}}$)。我们提出了一种XRF图像修复方法，以解决扫描时间长的问题，从而在加快扫描过程的同时，能够重建出高质量的XRF图像。将提出的自适应图像采样算法应用于扫描目标的RGB图像，生成采样模板。然后根据采样掩模驱动XRF扫描仪以扫描总图像像素的子集。最后，通过融合RGB图像来重建全扫描XRF图像，对扫描后的XRF图像进行内嵌。实验表明，所提出的自适应采样算法能够有效地对图像进行采样，并获得了比现有方法更好的重建精度。
This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). It consists of an adaptive sampling mask generation network which is jointly trained with an image inpainting network. The sampling rate is controlled by the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. In addition to the image sampling and reconstruction process, we show how it can be extended and used to speed up raster scanning such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved into lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposure requirements (e.g., ${\text{100}}\; \mu{\text{s}}-{\text{1 ms}}$ per point). We propose an XRF image inpainting approach to address the long scanning times, thus speeding up the scanning process, while being able to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of existing methods.