scholarly journals Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 244 ◽  
Author(s):  
Julio Mello Román ◽  
José Vázquez Noguera ◽  
Horacio Legal-Ayala ◽  
Diego Pinto-Roa ◽  
Santiago Gomez-Guerrero ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Infrared Image ◽  
Infrared Images ◽  
Public Database ◽  
Human Visual Perception ◽  
Multiple Features ◽  
Thermal Images ◽  
Low Contrast ◽  
Database Evaluation ◽  
Infrared Thermal Images

Discrete entropy is used to measure the content of an image, where a higher value indicates an image with richer details. Infrared images are capable of revealing important hidden targets. The disadvantage of this type of image is that their low contrast and level of detail are not consistent with human visual perception. These problems can be caused by variations of the environment or by limitations of the cameras that capture the images. In this work we propose a method that improves the details of infrared images, increasing their entropy, preserving their natural appearance, and enhancing contrast. The proposed method extracts multiple features of brightness and darkness from the infrared image. This is done by means of the multiscale top-hat transform. To improve the infrared image, multiple scales are added to the bright areas and multiple areas of darkness are subtracted. The method was tested with 450 infrared thermal images from a public database. Evaluation of the experimental results shows that the proposed method improves the details of the image by increasing entropy, also preserving natural appearance and enhancing the contrast of infrared thermal images.

scholarly journals Contour detection in an infrared image using the modified snake algorithm

2018 ◽  
Vol 226 ◽  
pp. 04049
Author(s):  
Viacheslav V. Voronin ◽  
Oxana S. Balabaeva ◽  
Marina M. Pismenskova ◽  
Svetlana V. Tokareva ◽  
Irina V. Tolstova
Keyword(s):  
Active Contours ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Modified Model ◽  
Thermal Images ◽  
Low Contrast ◽  
Security Applications ◽  
Image Contour ◽  
Snake Algorithm

Infrared and thermal images have been used widely in the different forensics and security applications. Such images show the temperature difference between different objects and scene background. One of the drawbacks of such images is low contrast and noisy images which should be enhanced. This paper presents a new thermal image contour detection algorithm using the modified snake algorithm. The segmentation algorithm based on the image enhancement and the modified model of active contours based on regions, taking into account the calculation of the anisotropic gradient. Some presented experimental results illustrate the performance of the proposed cloud system on real thermal images in comparison with the traditional methods.

scholarly journals MSIS: Multispectral Instance Segmentation Method for Power Equipment

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jun Shu ◽  
Juncheng He ◽  
Ling Li
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Feature Fusion ◽  
Infrared Image ◽  
Current Method ◽  
Power Equipment ◽  
Infrared Images ◽  
Low Contrast ◽  
Novel Structure ◽  
Instance Segmentation

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

Infrared Electric Image Enhancement Based On Fuzzy Renyi Entropy and Quantum Genetic Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1774-1780
Author(s):  
Song Hai Fan ◽  
Shu Hong Yang ◽  
Pu He ◽  
Hong Yu Nie
Keyword(s):  
Genetic Algorithm ◽  
Infrared Image ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Maximal Entropy ◽  
Infrared Images ◽  
Low Contrast ◽  
Quantum Genetic Algorithm ◽  
Entropy Principle ◽  
Electric Equipment

Infrared thermograph has been applied in electric equipment inspection widely, but the visual effects of infrared images are always undesirable. Considering the limitation of low luminance，low contrast in infrared images，an enhancement method based on fuzzy Renyi entropy and quantum genetic algorithm is presented in this paper．Firstly，the contrast-sketching function presented in [1] is improved based on the idea of segmentation. Then, in order to segment the infrared image, Renyi entropy is extend to fuzzy domain considering the fuzzy nature of infrared image, and is employed to threshold the infrared image following maximal entropy principle. In order to meet the real-time demand of online monitoring, quantum genetic algorithm is employed to search the optimal parameters of the transform function. The experimental results indicate that the method can well improve the visual effect of infrared electric images．

The Research of Non-Uniformity Correction of Infrared Image in Thermal Wave Testing Based on Homomorphism Technology

2011 ◽  
Vol 58-60 ◽  
pp. 2376-2380
Author(s):  
Yuan Jia Song ◽  
Wei Zhang ◽  
Zheng Wei Yang ◽  
Guo Feng Jin
Keyword(s):  
Thermal Wave ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Future Research ◽  
Infrared Images ◽  
Signal To Noise ◽  
Improve Image Quality ◽  
High Noise ◽  
Low Contrast ◽  
Homomorphic Filtering

The infrared thermal wave technology is a new nondestructive testing (NDT) method with a kind of advantage, including non-contact, intuitionistic, fast et al. But the infrared images always have defects that the low-contrast and high-noise due to uneven brightness and calefaction in the testing process, which enhance the difficulty of following quantitative distinguishment of defects. Therefore, the improved homomorphic filtering is given in this article. The detailed processes of the method and testing results are given. The results of the experiments show that the method has higher peak signal to noise ratio (PSNR), can improve image quality, which establish basis for future research of image segmentation.

Reflection interference removal for infrared thermography images based on GAN

2021 ◽  
Vol 63 (9) ◽  
pp. 529-533
Author(s):  
Jiali Zhang ◽  
Yupeng Tian ◽  
LiPing Ren ◽  
Jiaheng Cheng ◽  
JinChen Shi
Keyword(s):  
Neural Networks ◽  
Infrared Thermography ◽  
Metal Surfaces ◽  
Infrared Image ◽  
Infrared Camera ◽  
Infrared Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Continuous Application ◽  
Visible Images

Reflection in images is common and the removal of complex noise such as image reflection is still being explored. The problem is difficult and ill-posed, not only because there is no mixing function but also because there are no constraints in the output space (the processed image). When it comes to detecting defects on metal surfaces using infrared thermography, reflection from smooth metal surfaces can easily affect the final detection results. Therefore, it is essential to remove the reflection interference in infrared images. With the continuous application and expansion of neural networks in the field of image processing, researchers have tried to apply neural networks to remove image reflection. However, they have mainly focused on reflection interference removal in visible images and it is believed that no researchers have applied neural networks to remove reflection interference in infrared images. In this paper, the authors introduce the concept of a conditional generative adversarial network (cGAN) and propose an end-to-end trained network based on this with two types of loss: perceptual loss and adversarial loss. A self-built infrared reflection image dataset from an infrared camera is used. The experimental results demonstrate the effectiveness of this GAN for removing infrared image reflection.

scholarly journals Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators

2019 ◽  
Author(s):  
Han Xu ◽  
Pengwei Liang ◽  
Wei Yu ◽  
Junjun Jiang ◽  
Jiayi Ma
Keyword(s):  
Probability Distribution ◽  
State Of The Art ◽  
Infrared Image ◽  
Infrared Images ◽  
Generative Adversarial Network ◽  
Visible Image ◽  
Qualitative And Quantitative ◽  
Adversarial Network ◽  
Fused Image ◽  
Visible Images

In this paper, we propose a new end-to-end model, called dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions. Unlike the pixel-level methods and existing deep learning-based methods, the fusion task is accomplished through the adversarial process between a generator and two discriminators, in addition to the specially designed content loss. The generator is trained to generate real-like fused images to fool discriminators. The two discriminators are trained to calculate the JS divergence between the probability distribution of downsampled fused images and infrared images, and the JS divergence between the probability distribution of gradients of fused images and gradients of visible images, respectively. Thus, the fused images can compensate for the features that are not constrained by the single content loss. Consequently, the prominence of thermal targets in the infrared image and the texture details in the visible image can be preserved or even enhanced in the fused image simultaneously. Moreover, by constraining and distinguishing between the downsampled fused image and the low-resolution infrared image, DDcGAN can be preferably applied to the fusion of different resolution images. Qualitative and quantitative experiments on publicly available datasets demonstrate the superiority of our method over the state-of-the-art.

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