Комп’ютеризована система обробки та аналізу цифрових зображень, отриманих при електронно-променевому зварюванні

The problem of determination of junction lines of objects on images gained at electron-beam welding is considered. Image contrast enhancement is performed by three-stage method based on fuzzy logic. For the determination of welding trajectory locally adaptive approach to segmentation of junction lines of objects and tracing of their contours based on the analysis of image brightness characteristics changes are proposed. Joint сurve is represented analytically with optimal parametrical spline approximation using LSM approximation on each spline link.Розглянуто задачу визначення ліній стику об’єктів на зображеннях отриманих при електронно-променевому зварюванні. Контрастування зображення здійснюється три-етапним методом на основі нечіткої логіки. Для визначення траєкторії зварювання запропоновано локально-адаптивний підхід до сегментації ліній стику об’єктів та відслідковування їх контуру, що базується на аналізі зміни яскравісних характеристик зображення. Траєкторію стику у аналітичному вигляді представлено за допомогою оптимально параметричної сплайн-апроксимації з використанням середньоквадратичних наближень на кожній ланці сплайну

A Fuzzy Homomorphic Algorithm for Image Enhancement

The implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The technique combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of image enhancement. This algorithm reduces the computational complexity by eliminating the need for image-size-dependent filter kernels and the forward and inverse Fourier Transforms.   The proposed algorithm is compared with the more established algorithms for the enhancement of low contrast images with uneven illumination. The results show that the fuzzy method provides similar or better results than the frequency domain method and some other well-known image enhancement algorithms

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance

Random Adjustment - Based Chaotic Metaheuristic Algorithms for Image Contrast Enhancement

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem\u27s characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement using more detailed information of grayness of an image. However, these methods do not perform well on images taken in uncontrolled environment which pose different challenges such as illumination variation, perspective distortion and viewpoint variation. In this paper, we have worked to devise a more robust image enhancement method using fuzzy logic. We propose a novel multi scale entropy based measurement performed using fuzzy logic image processing and utilize it to define and enhance the contrast. For this purpose, we present a mathematical formula to calculate contrast using an adaptive amplification constant. Our approach uses both the local and global entropy information. We have experimented our algorithm on images from Crowd Counting UCF dataset, which contains very dense crowds and complex texture that stands in line with the challenges targeted in this paper. The results show an improved quality than original dataset images and prove that our method enhances the images with a more dynamic ranged contrast as well as better visual results

Image Enhancement Using a Contrast Measure in the Compressed Domain

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Contrast Enhancement for JPEG Images in the Compressed Domain

With the increase in digitization, there has been a great demand for data storage with effective techniques for data computation. As these days, a lot of data is being transferred over internet in the form of images, data storage is a prime concern, for which there is a requirement of image compression without losing the important details of the image. Digital image compression finds its applications in various fields like Medical, Automation, Defense, Photography etc. which also requires that the image produced should be visibly pleasing with sharp and clear details. The latter is achieved by a pre-processing technique called Image Enhancement.This research project is based upon the contrast enhancement of the color Images, where each color R-G-B channel is separately analyzed in the Y-Cb-Cr channel, in the compressed domain. The Discrete Cosine Transform is used as the compressed domain and further analysis is made on the block coefficients of the DCT where the block size considered is 8x8. Each DCT block contains one DC coefficient and 63 AC coefficients. The DCT coefficients are analyzed on the basis of their statistical behaviour. It is seen that the DC coefficient of each block DCT follow Gaussian distribution and the AC coefficients follow the Laplacian distribution .The DC coefficient being the mean value of the block DCT, is observed to be affecting the illumination of the image whereas the remaining 63 coefficients i.e. AC coefficients of the block DCT affected the contrast of the image. This thesis investigates a novel method for enhancing the image contrast based on the statistical behaviour of the block DCT coefficients. Furthermore, we use the concept of coefficient of variation (Cv) for arriving at a DC scaling factor required to modify the original DC coefficient value of each block. We also evaluate AC scaling factor by band analysis of each block based upon their contrast and entropy bands. The proposed work analyses both the DC coefficient and the 63 AC coefficients of each block separately