Automatic inspection of X-ray scans is a critical component of modern safety protocols. It plays an indispensable role in detecting concealed weapons, explosives, and other prohibited items that could pose a threat to public safety. Current surveillance systems perform poorly without human intervention. Therefore, many methods have been developed to address this practical problem. However, X-ray scans are complex, with objects often overlapping in a semi-transparent state, which limits the effectiveness of existing methods. To solve the inherent overlapping challenge of X-ray scans, we propose a plug-and-play module called the Foreground-aware Prototypical Network (FaPN). It encourages the model to focus more on prohibited items rather than other irrelevant components. Our proposed FaPN has several excellent properties. First, it is deterministic and discriminative for prohibited items, which is crucial for robust detection in the presence of overlapping. Additionally, it can adapt to multi-scale feature extraction by integrating with existing one-stage detectors such as YOLO series. Comprehensive experiments on datasets verify the effectiveness and efficiency of our model.

X-ray safety inspection machines are essential for maintaining security. In the past few years, the rapid development and widespread application of deep learning have significantly contributed to the progress of X-ray security detection. In order to achieve automatic detection of dangerous objects in X-ray baggage images, this paper proposes an efficient dangerous objects detection network called X-YOLO, which incorporates feature fusion and attention mechanisms specifically for X-ray baggage images. In the proposed network, an innovative attention mechanism module is integrated into the architecture, and an improved Dynamic Head module is designed to enhance object detection across various dimensions. In the experiments, the HiXray and OPIXray datasets are selected, and the experimental results show that the X-YOLO network demonstrates excellent performance when compared with several state-of-the-art networks. The mAP50 value reached 86.6% on the HiXray dataset and 93.7% on the OPIXray dataset, representing improvements of 2.4% on the HiXray dataset and 4.6% on the OPIXray dataset compared with the baseline.

Traditional Chinese medicines (TCMs) play an important role in the treatment of many diseases. For industrial production, classical TCMs identification methods suffer from high labor cost and low efficiency. Moreover the complex multi-object combinations of TCMs lead to serious feature confusion problem. In this article, we propose a novel detection network for TCMs called TCMnet. It focuses on the performance degradation caused by the images in different datasets containing different number of objects. First, an innovative multilevel feature fusion framework is proposed, which improves the generalization of the model. Then, a receptive field controlling architecture is established to limit the receptive field for reducing the confusion among multiple objects. Finally, a trainable feature resolution enhancement algorithm is proposed to increase the precision of classifier by enhancing local detail information. In the experiments, we choose 18 classes with 1800 images from our TCMs dataset. The experimental results show that TCMnet proposed in this article is able to mitigate the feature confusion problem in single-multiple object detection. In addition, TCMnet achieves a good accuracy compared with other detectors on single-object and multi-object detection tasks.

In the information age, image processing technologies play a vital role in the field of industrial engineering. In this article, a novel spline scheme called the hierarchical polishing splines algorithm is proposed, and it is applied to the field of computed tomography (CT) image reconstruction of coronavirus disease 2019 (COVID-19). The proposed algorithm defines a set of control lattices, wherein the density of lattice points in these control lattice ranges from coarse to fine in order, and the final applied function is produced by adding the functions derived from every control lattices. In order to demonstrate the performance of the proposed algorithm, some CT images from COVID-19 patients are selected. The experimental results show that the reconstructed COVID-19 CT images by using the proposed algorithm have good quality when compared with some widely used approaches. In addition, this article also applies the proposed algorithm to reconstruct the infected regions derived from COVID-19 CT images, and the results also show that the proposed algorithm is more efficient than others. Besides, the applicability of the proposed algorithm is discussed, wherein the COVID-19 severity is estimated based on the reconstructed COVID-19 CT images, and the applicability analysis shows that the use of the proposed algorithm to reconstruct COVID-19 CT images can help to achieve more accurate severity assessment of COVID-19 in a certain extent.

The lunar surface has complex geomorphic characteristics. Since the lunar terrain entropy can reflect the amount of geomorphic information contained in the lunar terrain, this article uses the ratio of the elevation value of a local point on the lunar surface to the total elevation value of the neighborhood to calculate the local terrain entropy value of the Moon. Then, the hierarchical polishing splines algorithm is proposed to construct the digital terrain entropy model (DTEM) of the Moon, wherein the new algorithm produces a sequence of functions based on a hierarchy of coarse-to-fine control lattices to generate the modeling function, which has good modeling performance. Using the proposed algorithm, multiscale DTEMs of the Moon are constructed based on square moving windows with different sizes. From the lunar DTEMs, it can be found that the lunar terrain entropy is sensitive to the size of the square moving window and the resolution of lunar DEM, and the high-resolution lunar DTEM with suitable moving window can well show topographical variations. In addition, the lunar terrain entropy distribution models are created based on the lunar DTEMs, which is significantly important to the study of the lunar terrain entropy distribution law. Besides, two terrain parameters, i.e., surface roughness and surface slope, are selected to show that the geomorphic characteristics of the Moon can be well reflected by the lunar terrain entropy.

In the context of the fourth industrial revolution (also referred to as Industry 4.0), industrial image processing forms a valuable basis for newly conceived applications and services, especially image reconstruction as one of the key technologies of industrial image processing plays an important role in industrial engineering. To develop an effective method for industrial image reconstruction, this article proposes the optimized hierarchical many-knot (HMK) splines (abbreviated as OHMK splines) method. In the scheme, a fine merged control lattice is derived from the hierarchy of coarse-to-fine control lattices, and then the final reconstruction function obtained by OHMK splines is the function generated on the merged fine control lattice, which is not obtained by superimposing all functions generated on the coarse-to-fine control lattices. Therefore, the computation for the final reconstruction function relies on the number of control points in the merged fine control lattice, not depending on all the control points in the hierarchy. Through the experimental results, we can find that the good reconstruction performance for the given image can be obtained by using the OHMK splines method, and the computational overhead of the OHMK splines method is superior to that of the HMK splines method.

Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of coronavirus disease (COVID-19), in this article, we propose a new method that identifies rich features of lung infections from a chest computed tomography (CT) image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.

The recognition of vehicle license plate is an important part of the modern intelligent traffic management system, which has been widely used in many fields. On the Hong Kong-Zhuhai-Macao Bridge, the vehicles may have multiple license plates (LPs) with three different styles, and the traditional contour-based vehicle license plate recognition methods cause a considerable miss rate for multi-style license plates. With such a background, this paper proposes a multi-style license plate recognition method based on feature pyramid network with instance segmentation, which translates the license plate recognition into object instance detection and gets rid of the steps of segmentation and optical character recognition of traditional methods. In the scheme, we design a novel license plate recognition network to precisely locate and classify characters and LP regions concurrently, wherein an assembly layer is added for combining the characters into license plates and outputting license plate strings. The experimental results show that the proposed method achieves 98.57% recognition rate of multi-style LPs on the real world applications. Moreover, we also select the standard license plate datasets, that only contain single style license plates, to test the proposed license plate recognition method, and the corresponding results show that the proposed method achieves competitive performance.

Image representation is an active research area in the field of image processing. This paper proposes a novel image representation method under a non-standard positional numeral system, wherein complex numbers are used as bases in such non-standard positional numeral system. It is different with binary code and decimal code, where the digit 2 is used as the base in the binary system, and the digit 10 is used as the base in the decimal system. In the proposed image representation method, a two-dimensional image is transformed into a one-dimensional 0$\sim$1 sequence, and its Gaussian integer is calculated based on the derived one-dimensional 0$\sim$1 sequence. On the contrary, the original two-dimensional image can be recovered from its Gaussian integer. When images are represented as Gaussian integers, the classical geometrical operations are introduced into image processing. Then, the relationship of different images is established by the methods of plane geometry, i.e., the addition, subtraction, multiplication, division, conjugate, and inverse operations of images are defined. The experimental results show that the selection of complex number as base in the positional numeral system is a very special coding method, a given digital image is effectively converted to a Gaussian integer by using the proposed image representation method, and the image arithmetic is also successfully achieved. In addition, three applications based on the proposed image representation method including image camouflage, image sharing, and image scrambling are selected to demonstrate that the new image representation has good and potential practical applicability in the field of secure encryption of digital images.

The microwave radiometer (MRM) on-board the Chinese Chang'e-2 (CE-2) lunar probe measures the lunar brightness temperature (also referred to as TB) data that are large-scale scientific data. In order to construct lunar TB map, the optimized hierarchical MK splines method is proposed, which uses a hierarchy of coarse-to-fine control lattices to generate a fine control lattice. The computation of the TB construction function is limited to the small number of control points in the merged control lattice, and then the desired high-resolution TB maps are constructed. At the same time, some basis relations between the lunar TB and frequencies are also analyzed based on the constructed TB maps. It can be found that the high-frequency TB map shows lunar topographic features with close similarity. Furthermore, to express the TB distribution features quantitatively, the lunar TB distribution models, including the global TB model of the Moon, the TB model of the lunar far side, and the TB model of the lunar near side, are established based on the constructed TB maps, and the obtained TB distribution models are log-normal distributions. The establishment of the lunar TB distribution model is important to reasonably select the color layer and intensity of color for the lunar TB maps, and is helpful for studying the lunar TB distribution law. In addition, the topographic data measured by the lunar orbiter laser altimeter are selected to discuss the influence of elevation on the lunar TB, and the CE-2 TB data combined with the FeO and TiO2abundances are used to study the microwave thermal emission features of the lunar regolith. The research results have important implications for studying the thermal radiation of the Moon.

The brightness temperature (TB) data of the Moon acquired by the microwave radiometer (MRM) on-board the Chinese Chang'e-2 (CE-2) lunar probe are valuable and comprehensive data, which can be helpful in studying the physical properties of the lunar regolith, such as thickness, physical temperature, and dielectric constant. To construct the accurate and high-resolution lunar TB model with the TB data obtained by the MRM on-board CE-2, 2401 tracks of the original TB data are quantized by using the hour angle processing, and the hierarchical MK splines function (HMKSF) method is presented, which uses a hierarchy of coarse-to-fine control lattices to generate a sequence of TB model functions. The TB model constructor is the sum of the TB model functions derived at each level of the hierarchy. In addition, the lunar TB models with a resolution of 0.5°×0.5° in all four frequency channels are constructed for both the daytime and the nighttime. The obtained models show rich information, e.g., the global distribution of TB over the lunar surface, the effect of frequency on the TB model.

In the era of Big Data, it is very important to study large-scale data fitting methods. In order to ensure the calculation speed and accuracy, we propose a new kind of hierarchical many-knot splines (hereinafter called “hierarchical MK splines,” generally abbreviated as HMK splines) in this paper. The HMK splines method produces a sequence of MK spline functions. These MK spline functions are constructed into one ideal interpolation function by the MK spline refinement. In the case of regular sampling data, HMK splines can achieve the purpose of accurate approximation for the given data points without solving systems of equations. Further, in order to deal with the issues of scattered data fitting, the use of least-squares method will lead to the necessary of solving a linear system of equations. Since the ill-conditioned systems of equations often lead to unacceptable deviation of calculation results, one tries to avoid it as much as possible. The HMK splines algorithm can meet this requirement; it can avoid the intolerable deviation caused by solving systems of equations. Experimental results show that large-scale scattered data fitting can be easily achieved by the HMK splines algorithm and the reconstruction of nonuniform samples has a high accuracy.