Contrast Enhancement for Multi exposure Infrared Images

Entropy

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 experime...

Defence Science Journal, 2014

Low contrast and noisy image limits the amount of information conveyed to the user. With the proliferation of digital imagery and computer interface between man-and-machine, it is now viable to consider digital enhancement in the image before presenting it to the user, thus increasing the information throughput. With better contrast, target detection and discrimination can be improved. The paper presents a sequence of filtering operations in frequency and spatial domains to improve the quality of the thermal infrared (IR) images. Basically, two filters-homomorphic filter followed by adaptive Gaussian filter are applied to improve the quality of the thermal IR images. We have systematically evaluated the algorithm on a variety of images and carefully compared it with the techniques presented in the literature. We performed an evaluation of three filter banks such as homomorphic, Gaussian 5×5 and the proposed method, and we have seen that the proposed method yields optimal PSNR for all the thermal images. The results demonstrate that the proposed algorithm is efficient for enhancement of thermal IR images.

2021

This paper presents an efficient technique for enhancement of IR images, its basic idea is to use nonlinearities applied on the main trend in IR images extracted through a LPF and the details extracted through a HPF. Magnification of the details is applied, while damping of the main trend or local mean is also accomplished to attenuate the darkness effect in IRimages. Anon-linear model is applied anther trend and gain factor is applied to details .this gain is also derived from the local mean of the image the proposed approach succeeded in quality enhancement of IR images

Menoufia Journal of Electronic Engineering Research, 2020

This paper presents an efficient technique for enhancement of IR images, its basic idea is to use nonlinearities applied on the main trend in IR images extracted through a LPF and the details extracted through a HPF. Magnification of the details is applied, while damping of the main trend or local mean is also accomplished to attenuate the darkness effect in IR images. Anon-linear model is applied anther trend and gain factor is applied to details .this gain is also derived from the local mean of the image the proposed approach succeeded in quality enhancement of IR images

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIV, 2013

Recognition and identification ranges are limited to the quality of the images. Both the received contrast and the spatial resolution determine if objects are recognizable. Several aspects affect the image quality. First of all the sensor itself. The image quality depends on the size of the infrared detector array and the sensitivity. Second, also the intervening atmosphere, in particular over longer ranges, has an impact on the image quality. It degrades the contrast, due to transmission effects, as well as it influences the resolution, due to turbulence blur, of the image. We present studies in the field of infrared image enhancement. Several techniques are described: noise reduction, super resolution, turbulence compensation, contrast enhancement, stabilization. These techniques operate in real-time on COTS/MOTS platforms. They are especially effective in the army theatre, where long horizontal paths, and short line-of-sight limited urban operations are both present. Application of these techniques on observation masts, such as on military camp sites, and on UAVs and moving ground vehicles are discussed. Examples will be presented from several trials in which these techniques were demonstrated, including the presentation of test results.

Optical Engineering

Implementation of the Johnson criteria for infrared images is the probabilities of a discrimination technique. The inputs to the model are the size of the target, the range to it, and the temperature difference against the background. The temperature difference is calculated without taking the background structure into consideration, but it may have a strong influence on the visibility of the target. We investigated whether a perceptually based temperature difference should be used as input. Four different models are discussed: 1. a probability of discrimination model largely based on the Johnson criteria for infrared images, 2. a peak signal-to-noise ratio model, 3. a signal-to-clutter ratio model, and 4. two versions of an image discrimination model based on how human vision analyzes spatial information. The models differ as to how much they try to simulate human perception. To test the models, a psychophysical experiment was carried out with ten test persons, measuring contrast threshold detection in five different infrared backgrounds using a method based on a two-alternative forced-choice methodology. Predictions of thresholds in contrast energy were calculated for the different models and compared to the empirical values. Thresholds depend on the background, and these can be predicted well by the image discrimination models, and better than the other models. Future extensions are discussed.

International Journal of Advances in Computer Science and Technology , 2022

Infrared (IR) images are widely used in many fields such as medical imaging, object tracking, astronomy and military purposes for securing borders. Infrared images can be captured day or night based on the type of capturing device. The capturing devices use electromagnetic radiation with longer wavelengths. There are several types of IR radiation based on the range of wavelength and corresponding frequency. Due to noising and other artifacts, IR images are not clearly visible. In this paper, we present a complete up-to-date survey on IR imaging enhancement techniques. The survey includes IR radiation types and devices and existing IR datasets. The survey covers spatial enhancement techniques, frequency-domain based enhancement techniques and Deep learning-based techniques.

Recognition and identification ranges are limited to the quality of the images. Both the received contrast and the spatial resolution determine if objects are recognizable. Several aspects affect the image quality. First of all the sensor itself. The image quality depends on the size of the infrared detector array and the sensitivity. Second, also the intervening atmosphere, in particular over longer ranges, has an impact on the image quality. It degrades the contrast, due to transmission effects, as well as it influences the resolution, due to turbulence blur, of the image. We present studies in the field of infrared image enhancement. Several techniques are described: noise reduction, super resolution, turbulence compensation, contrast enhancement, stabilization. These techniques operate in real-time on COTS/MOTS platforms. They are especially effective in the army theatre, where long horizontal paths, and short line-of-sight limited urban operations are both present. Application of these techniques on observation masts, such as on military camp sites, and on UAVs and moving ground vehicles are discussed. Examples will be presented from several trials in which these techniques were demonstrated, including the presentation of test results.

2015

The detection of weapon concealed underneath a person’s cloths is very much important for the security of the public as well as the safety of public assets like airports, buildings, and railway stations etc. The goal is to develop an automatic detection and recognition system of concealed weapons using sensor technologies and image processing. The goal of this paper is to present the Concealed Weapon Detection method by infrared imaging (IR). Normal image is the human perception vision, whereas the IR image produces the information about concealed weapon. Fusion of normal and IR image gives the relevant information of these two images. Segmentation is applied for fused image using Adaptive K-means clustering to clearly distinguish the concealed weapon from the surrounding. The detected Concealed Weapon will be fit into the bounding box.

Multimedia Tools and Applications, 2020

This paper presents new three proposed approaches for enhancement of Infrared (IR) night vision images. The first approach is based on merging gamma correction with the Histogram Matching (HM). The second approach depends on hybrid gamma correction with Contrast Limited Adaptive Histogram Equalization (CLAHE). The third approach is based on a trilateral enhancement that the IR images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on Optimum Global Thresholding (OTSU) method. The second stage, which is the heart of the enhancement approach, depends on Additive Wavelet Transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while Plateau Histogram Equalization (PHE) is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high pass filter. Average gradient, Sobel edge magnitude, entropy and spectral entropy has been used as quality metrics for assessment the three proposed approaches. It is clear that the third proposed approach gives superior results to the two proposed approaches point view the quality metrics. On the other hand, clear that the third proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The first proposed approach gives better results to the two proposed approaches from the computation time perspective.