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Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives. [ABSTRACT FROM AUTHOR]

This Special Issue covers a broad range of topics in infrared (IR) vision technology, including novel sensing techniques, advanced signal processing algorithms, and innovative applications. The papers in this Special Issue are as follows: Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique [[1]]: This paper proposes a novel convolutional neural network (CNN)-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. Dear readers and fellow researchers, I am pleased to introduce this Special Issue of I Sensors i , entitled "Sensing and Processing for Infrared Vision: Methods and Applications". [Extracted from the article]

Infrared search and track (IRST) systems have attracted more attention in recent years due to advances of infrared imaging technology. In a typical IRST system, small infrared detection algorithm is the most challenging part of the system development procedure. In this paper, an effective algorithm based on the average absolute gray difference (AAGD) is presented. In the first step, the main deficiencies of the AAGD algorithm are investigated precisely. After identifying three major drawbacks of the AAGD algorithm, a powerful small target detection algorithm is developed through compensating every single weak-spot in the AAGD algorithm. The simulation results on real infrared images prove that the proposed algorithm not only compensates the AAGD disadvantages but also outperforms the recently published well-known small infrared target detection algorithms in both qualitative and quantitative perspectives. [ABSTRACT FROM AUTHOR]

False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources. [ABSTRACT FROM AUTHOR]

Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm. [ABSTRACT FROM AUTHOR]

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction. [ABSTRACT FROM AUTHOR]

• A fast and robust algorithm for small infrared target detection is proposed. • Directional approach is used to develop a novel algorithm called ADMD. • Both noise and background clutter are suppressed, effectively. • The proposed method is implemented in an efficient manner. Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

When designing a system-on-chip (SOC), a network-on-chip (NOC) paradigm is the backbone of used interconnection, but in recent years, with a great improvement in silicon technology and the ability to implement billions of transistors on a wafer, it sounds that wire-based communication is not efficient any longer, therefore designers intend to replace wireless transferring data methodology instead. In the following, we show how the performance of the wireless network is improved with subnet extension. This paper focuses on the evaluation of delay and throughput which are two important factors in network proficiency. In fact, to enhance the performance of the system, we need to reduce the number of delay cycles and improve the throughput, therefore to keep balance between these two parameters, designer has to adjust the packet injection rate (PIR) in a safe margin such that it does not exceed a certain point in each state, otherwise delay is uncontrollable, thus it is required to clearly identify the take-off points. About the designer also has to be informed purpose for which the system is going to be designed. It strictly depends on whether high throughput or low delay cycle is desirable. Subnet extension is a way to achieve this target. Here, totally three networks with the number of cores 64, 512 and 1024 have been selected, respectively. The effect of subnet extension is evaluated on each one. The behavior of each network with different number of subnets and IPs is studied. Obtained results from the simulator for different ranges of PIR and subnets are significant. To emphasize, we highlight the take-off points for the delay cycles and the points which does not cross the PIR level. Performance evaluation is conducted based on flit-accurate and open source system C simulator BookSim. [ABSTRACT FROM AUTHOR]

• A new method based on the particle swarm optimization algorithm is introduced for detecting small targets in infrared images. • The computational complexity of the proposed algorithm is not linear to number of pixels in the input image. • The proposed method is capable to operate on a large-scale image in a relatively small timeslot. In this paper, a new method is introduced for detecting small targets in infrared images. The proposed method is based on the particle swarm optimization algorithm (PSO). Considering the nature of the small target detection problem, a dynamic optimization algorithm is developed to detect targets. The proposed algorithm is called the dynamic particle swarm detector (DPSD). Unlike common small target detection algorithms, the computational complexity of the proposed algorithm is not linear to number of pixels in the input image. Therefore, it is capable of operating on a large-scale image in a relatively small timeslot. Many experiments are carried out to evaluate the effectiveness of the proposed method, where the DPSD is compared to seven well-known methods in terms of quantitative detection metrics. The results show that the proposed detector outperforms the baseline algorithms. [ABSTRACT FROM AUTHOR]

Infrared small target detection is a challenging task. Despite the recent advances in the development of small infrared target detection algorithms, having a robust target detection algorithm with high probability of detection rate remains unaddressed. To this end, in this paper, a morphological top-hat transform is presented. The proposed method benefits from intrinsic high probability detection rate of classical top-hat transform, while the false alarm rate remains below the acceptable threshold. Also, the sensitivity to noise analysis demonstrates that the proposed method is robust against various noise intensities. The proposed method is tested on some real infrared images. The experiments show that the proposed method outperforms state-of-the-arts in both quantitative and qualitative manners. [ABSTRACT FROM AUTHOR]