DCT Coefficients Weighting (DCTCW)-Based Gray Wolf Optimization (GWO) for Brightness Preserving Image Contrast Enhancement

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.

Key Points’ Location in Infrared Images of the Human Body Based on Mscf-ResNet

The human body generates infrared radiation through the thermal movement of molecules. Based on this phenomenon, infrared images of the human body are often used for monitoring and tracking. Among them, key point location on infrared images of the human body is an important technology in medical infrared image processing. However, the fuzzy edges, poor detail resolution, and uneven brightness distribution of the infrared image of the human body cause great difficulties in positioning. Therefore, how to improve the positioning accuracy of key points in human infrared images has become the main research direction. In this study, a multi-scale convolution fusion deep residual network (Mscf-ResNet) model is proposed for human body infrared image positioning. This model is based on the traditional ResNet, changing the single-scale convolution to multi-scale and fusing the information of different receptive fields, so that the extracted features are more abundant and the degradation problem, caused by the excessively deep network, is avoided. The experiments show that our proposed method has higher key point positioning accuracy than other methods. At the same time, because the network structure of this paper is too deep, there are too many parameters and a large volume of calculations. Therefore, a more lightweight network model is the direction of future research.

Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram

The problem of reducing the dynamic range and improving the quality of infrared (IR) images with a wide dynamic range for their display on a liquid crystal matrix with 8-bit pixels is considered. To solve this problem in optoelectronic devices in real time, block algorithms based on local equalization of the histogram are widely used, taking into account their relatively low computational complexity and the possibility of taking into account local features of the brightness distribution. The basic adaptive histogram equalization algorithm provides reasonably high image quality after conversion, but may result in excessive contrast for some types of images. In a modified algorithm of adaptive histogram equalization, the contrast is limited by a threshold by truncating local maxima at the edges of the histogram. This leads, however, to a deterioration in other indicators of image quality. This disadvantage is inherent in many algorithms of local histogram equalization, along with limited control over the characteristics of image reproduction quality. To improve the quality and expand the control interval for the characteristics of the reproduction of infrared images, the article proposes an algorithm for double reduction of the dynamic range of the image with intermediate control of the shape of its histogram. This algorithm performs: preliminary reduction of the dynamic range of the image based on adaptive equalization of the histogram, control of the shape of the histogram based on its linear or nonlinear compression, linear stretching of its central part and linear stretching (compression) of its lateral parts, final reduction of the dynamic range based on linear compression of the entire histograms. The characteristics of the proposed algorithm are compared with the characteristics of known algorithms for reducing the dynamic range and improving the image quality. The dependences of the characteristics of the quality of image reproduction after a decrease in their dynamic range on the control parameters of the proposed algorithm and recommendations for their choice taking into account the computational complexity are given.

ISSUES AND COUNTERPLAN OF EVACATION CENTER AT NIGHT - CASE STUDY OF NORTHEN GYMNASIUM IN SUZAKA CITY AFFECTED BY TYPHOON HAGIBIS IN 2019 -

The living environment at the evacuation center is very important for ensuring the health, safety and security of the evacuees, but at present, preparations for providing a satisfactory living environment are not enough. In order to improve that, we have to consider various conditions, such as "ensure the lighting environment for the safe and secure and sleep," "toilet ensure," "ensure the thermal environment". In this study, we investigated the actual condition of glare in the nighttime lighting environment of evacuation center and examined the improvement method. Regarding the glare improvement method, the effectiveness of indirect lighting was shown based on the results of brightness distribution simulation with Radiance.

Skin Lesion Detection Algorithms in Whole Body Images

Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.

Magnetic Domain Structure in Ferromagnetic Kagome Metal DyMn6Sn6

Two types of magnetic domains, that is, type-I domain belt domain and type-II new stripe domain, are observed in a kagome metal DyMn6Sn6 by microscopic magneto-optic Kerr imaging technique. From 255 to 235 K, the spin reorientation is observed directly in DyMn6Sn6. We analyze the structure of two types of domains through brightness distribution of the images. The type-II domain exists from 235 to 160 K by zero-field cooling (ZFC). At the same time, type-I domain and type-II domain coexist and transform into each other with variation of temperature. Type-II domains can easily transform into type-I domains when the temperature and magnetic field changes, and this process is irreversible. These results demonstrate that the type-I domain is more stable than the type-II domain. The phase diagram of magnetic domains in DyMn6Sn6 is obtained.

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a Convolutional Neural Network (CNN) that analyses the outputs of semi-analytic methods which parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialised lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically re-initialise the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sérsic sources, accurately classifies source reconstructions of the same type with a precision P &gt; 0.99 and recall R &gt; 0.99. The same CNN, without re-training, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed HUDF sources. Using the CNN predictions to re-initialise the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.

Investigation of the Algorithm to Find Defects in High-voltage Insulators for an Automated Thermal Imaging Control System

This paper describes a software algorithm for detecting defective insulating structures using infrared images. The defect detection criteria are based on a joint analysis of the mean and standard value of the brightness distribution of a set of insulators. The effectiveness of the developed criteria is substantiated by the results of laboratory tests of a significant number of insulators removed from high-voltage lines according to the results of thermal imaging diagnostics. Simultaneous analysis of thermograms of the same type of insulating structures according to the proposed algorithm is more effective in comparison with the subjective assessment of each of them separately, which was used earlier. In addition, this approach allows to reduce the time of analysis and decision-making based on the results of diagnostics.

Lighting Quality Criteria Based on the Luminance Spatial-Angular Distribution

The article is devoted to assessing lighting quality based on lighting engineering design's spatial-angular brightness distribution (LSAD). The main problems of modern lighting design related to the modeling of scenes based on the emissivity equation and restrictions on the use, in this regard, as the main criterion for the lighting quality of Unified Glare Rating (UGR), are considered. The mathematical foundations of the use of LSAD in the practice of lighting engineering design are proposed. The integral equation LSAD is obtained, which allows modeling the brightness at an arbitrary point of the scene volume. A method for solving the formulated equation based on double local estimates of the Monte Carlo method is proposed. The formulated algorithm for calculating LSAD is view-independent: LSAD visualizes the lighting scene at all fixed points. Methods for storing the calculated LSAD are proposed. Based on the LSAD, a new criterion for lighting quality is formulated, which was experimentally tested in a full-scale experiment to evaluate the lighting of Moscow Metro stations. In the experiment, the proposed criterion and UGR were compared. One calculated the quality criteria by the station's photos, and their correlation with observers' assessment was found. Computer models of stations were created, in which quality criteria were also calculated from the experiment viewpoints – obtained good correspondence of the calculations with the estimates of observers.