# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
This only recovers high-quality 3D information of the region of interest and leads to challenging image reconstruction problems, as truncation artifacts can hamper the reconstruction quality [11]. * In some cases, it is possible to move the detector while performing a scan, creating a large projection by stitching the images for several detector positions [12]. This permits capturing the full object at a high zoom factor, yet at the cost of a strong increase in radiation dose, scanning time, and computation time. The resolution of the reconstruction can also be increased by incorporating certain prior knowledge about the scanned object into the reconstruction algorithm, resulting in super-resolution reconstruction. In CT, such prior knowledge comes in many forms, e.g., sparsity of the reconstructed image [13], sparsity of its gradient [14; 15], or knowledge of the materials constituting the object and their attenuation coefficients [16]. However, such prior knowledge is often not available in practice and introduces the risk of making assumptions that are not in good agreement with the actual scanned object, which can decrease the quality of the reconstructed image. In recent years, machine learning has shown the ability to improve resolution in a range of imaging applications [17; 18]. In particular, convolutional neural networks (CNN) have been applied successfully to attain super-resolution for a wide range of imaging modalities [17]. By training the CNN to compute the mapping between low-resolution data and specially obtained high-resolution training data, the characteristics of the datasets can be learned, removing the need for manually choosing a model. However, this approach relies on the availability of high quality training data for a series of similar objects, which for tomography would consist of high- and low-resolution scans of these objects. Such data are often difficult to obtain in practice for two main reasons:  
* when the objects under investigation are unique (i.e., no batches of similar objects are available), it is not possible to obtain the training data;
* creating high-resolution scans requires long scanning time and may also require high radiation dose, which can be unacceptable for dose-sensitive objects. Therefore, applying existing machine learning approaches to achieve super-resolution in tomographic imaging is often infeasible in practice. In this paper, we propose a novel technique for computationally improving image resolution in tomography. The technique integrates a specially designed scheme for acquiring the scan data and a machine learning method for super-resolution imaging. The data acquisition protocol ensures that certain sub-regions of the scanned object can be reconstructed at high resolution, while still keeping the total scanning time and dose relatively low. By using these high-resolution sub-regions as a training target for creating a super-resolution version of the full object, key morphological properties of the scanned object can be captured even if the scanned object is unique and no similar objects are available. Rather than exploiting similarity between a large batch of objects, our approach exploits self-similarity within a single object. As such, it is expected to perform properly if the object has high self-similarity, meaning that the local structure of the material is consistent throughout the entire object. Objects with such self-similar structures are abundantly available in nature, and are investigated in detail specifically for their structure. Examples include the crystallization of rare fossils and meteorites, or the porosity and texture of rocks and soils in geology [10]. Furthermore, in materials science, investigations are conducted on the micro-structure of metal foams, batteries, and other materials to improve their physical properties [5; 19]. The goal of this paper is to present an approach for improving the resolution of tomographic reconstructions of these type of objects.