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This study introduces a novel methodology for electrical discharges recognition elaborating an algorithm based on the RGB colour image model and artificial neural network (ANN) classifier. The developed RGB-ANN algorithm aims to detect and monitor the propagation of electrical discharges until flashover, through analysis of six colours appearing in the discharges images, extracted from the flashover videos recorded on a plan glass insulator model under uniform pollution. First, more than 300 colours images are collected and divided into sets to form a large database. Using an RGB encoding system, each pixel is represented by (R, G, B) coordinates and each image is encoded by 3D matrix. For the discharge image, the coordinates of each pixel are compared to all database ones. The colour of the database having the same coordinates of the discharge image pixel is attributed to this latter. Based on the ratio of the pixels number of a given colour to the total pixels number of the discharge image, six indicators are quantified and grouped to form a feature vector. This latter is used as input of the ANN, in order to classify the evolution of discharges into five classes. As the main result, >98% of images have been well classified.

This paper proposes a new lossless encryption and decryption method for digital colour-image using 2D discrete Haar wavelet transform (DHWT) and 3D logistic chaotic map. The proposed cryptosystem p...

In comparison with integer-order chaotic systems, fractional-order chaotic systems exhibit more complex dynamics. In recent years, research into fractional chaotic systems for the utilization of image cryptosystems has become increasingly highlighted. This paper describes the development, testing, numerical analysis, and electronic realization of a fractional-order memcapacitor. Then, a new four-dimensional (4D) fractional-order memcapacitive hyperchaotic system is suggested based on this memcapacitor. Analytically and numerically, the nonlinear dynamic properties of the hyperchaotic system have been explored, where various methods, including equilibrium points, phase portraits of chaotic attractors, bifurcation diagrams, and the Lyapunov exponent, are considered to demonstrate the chaos behaviour of this new hyperchaotic system. Consequently, an encryption cryptosystem algorithm is used for colour image encryption based on the chaotic behaviour of the memcapacitive model, where every pixel value of the original image is incorporated in the secret key to strengthen the encryption algorithm pirate anti-attack robustness. For generating the keyspace of that employed cryptosystem, the initial condition values, parameters, and fractional-order derivative value(s) (q) of the memcapacitive chaotic system are utilized. The common cryptanalysis metrics are verified in detail by histogram, keyspace, key sensitivity, correlation coefficient values, entropy, time efficiency, and comparisons with other recent related fieldwork in order to demonstrate the security level of the proposed cryptosystem approach. Finally, images of various sizes were encrypted and recovered to ensure that the utilized cryptosystem approach is capable of encrypting/decrypting images of various sizes. The obtained experimental results and security metrics analyses illustrate the excellent accuracy, high security, and perfect time efficiency of the utilized cryptosystem, which is highly resistant to various forms of pirate attacks.

In this study, we derive the expressions of the minimal norm least-squares solution for the reduced biquaternion (RB) matrix equation AX=B by using the e1−e2 form of RB matrices, and the Moore–Penr...

In this paper, we introduce a total variation l 1 - l 2 regularization scheme with adapting the parameter for image restoration involving blurry and noisy colour images. Numerically, an efficient augmented Lagrangian method associated with alternating minimization method is described to obtain the optimal solution recursively. We provide the convergence analysis for the resulting algorithm. Experimental results show that our proposed model and algorithm have good signal to noise ratio (SNR) and improvement in signal to noise ratio (ISNR) values for a motion blur with different kinds of noises.

Helminthiases disease is one of the diseases that leads to a significant impairment in mental and physical development in children and adolescents. It entail risks if not treated properly. Hence, it is very important to have a system that can diagnose the helminthiases disease efficiently. Hence, the goal of this research is to analyse the segmentation performance for the unsupervised colour image segmentation of human intestinal parasites based on helminth between standard $k$ -means (KM) clustering algorithm and Fast $k$ -means (FKM) clustering algorithm through modified global contrast stretching (MGCS) enhancement technique and colour conversion based on saturation (S) component. A total of 200 helminth parasite images have been analysed with the technique proposed, which consists of 50 images for each species of Ascaris Lumbricoides Ova (ALO), Entrobious Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). Both KM and FKM have succeeded in segmenting the helminth parasites with almost similar results. However, FKM has surpassed KM in terms of processing time. FKM takes 694 seconds to finish the segmentation process, as compared to KM which takes 1112 seconds. Overall, FKM clustering has successfully segment the helminth parasites with less processing time and good segmentation performance of 99.33% for accuracy, 93.36% for sensitivity of and 99.43% for specificity. The results obtained shows that the approach technique is fast and suitable to segment the helminth parasites.

Helminthiases disease has been diagnosed as one of the most common health problems in children and adults that is caused by human intestinal parasites named helminth. An immediate diagnosis system is required for the identification and classification of helminth to avoid the development of parasites in human intestine. In response to the needs for a rapid and precise identification of helminth, this research proposes analyzing the segmentation performance analysis of unsupervised colour image segmentation of helminth parasites through enhanced $k$ -means clustering (EKM) algorithm on the modified global contrast stretching (MGCS) and the modified linear contrast stretching (MLCS) enhancement techniques. By segmenting the helminth parasites using EKM clustering algorithm, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented image can be obtained. A total of 100 helminth parasites images have been analyzed with the approach comprising of 50 images from each species of the Ascaris Lumbricoides Ova (ALO) and the Trichuris Trichiura Ova (TTO). Overall, the average results generated by the enhanced $k$ -means clustering algorithm on MGCS is higher than MLCS. The proposed technique achieved 99.88% accuracy, 95.68% sensitivity alongside 99.95% specificity. Ultimately, the result demonstrates that the proposed technique is suitable for image segmentation of the helminth parasites images.

Anyone with average knowledge about image editing software can modify an image and make people believe it is the real image. One type of image modification is to copy a portion of an image, change its brightness, and paste it onto another location. In this paper, we are intending a method to perceive the brightness changes in the copy-pasted region of a colour image by applying LBP (Local Binary Pattern) on all three channels R(red), G(green), and B(blue) of an image. After the application of the LBP, the image is separated into overlapping blocks, and those blocks with equal LBP values in each channel indicate brightness or contrast modified pasted regions. Each of the channels detected regions are combined to identify brightness change in the duplicated region of the image.

The main objective of this paper is to acquaint the reader with a trailblazing pike into the reciprocity axioms of the multidimensional discrete Hartley transform (DHT) through the multidimensional discrete Fourier transform (DFT). Spin-offs for the reciprocity axioms of multidimensional DHT are tendered through the reciprocity axioms of the multidimensional DFT. This is due to the mathematical hookup betwixt the DFT and DFT. DHT is a real transform, whilst the DFT is a complex transform. Real transforms are posthaste in implementation in contrast to the complex transforms. Ergo, the axioms deduced for the reciprocity of multidimensional DHT in this paper, can be used in real time for the implementation of reciprocity axioms of multidimensional DFT at a brisk pace, by half of the cent percentage than their actual use. Thence, the axioms for multidimensional DHT can be used in real-time applications that use multidimensional DFT such as digital video processing, colour image processing.

This paper deals with color image processing technique. Different image parameters are controlled by different mathematical functions. The processing using Verilog has an advantage of rapidity then reconfigurability over system processing, precisely essential for image filtering operation. The purpose is to process the image using Threshold functionality, Blueness performance and input. Fast actions and effective simulation have been measured though manipulative the algorithm. The simulation is passed out by establishing an association between MATLAB and Hardware Descriptive Language (HDL). Displayed result shows the accuracy of the image Brightness, Threshold and Invert operation.

Colorization is the process of converting a black and white image - lot of different shades of grey - into a realistic colour image. Most of the published works uses data sets with colour images and turns these into greyscale at training stage. CNN are quite good in classification task and can easily recognize skies, trees and foliage, faces and persons, and usually colorization works well for these elements, but fails to give accurate colour to a car, mostly because if a car is highlighted in an image, it’s a sport car and all sport cars are red.! Improvement is possible by using a pre-trained network for colorization and further strengthen the training process by adding a palette of relevant (salient) colours to the monochrome image. Our palette generation emphasizes salient colours aside from memorable colours (sky blue, leaf green), the latter are generally well recreated by most pre-trained CNNs devoted to colorization. When the network is finally used, the user defines some hints (i.e. small colour palette) prior to the colorization tasks. Unlike manual colorization these hints are provided once for a collection of images, making the technique suitable for movie colorization.

In 2019, the authors of the present paper presented a detailed study on the genus Diarsia Hübner, 1816, giving an overview on the distribution, research history and the comprehensive studies and generic classification of the genus, including the description of seven new species (Gyulai & Saldaitis, 2019). In that paper, one of the newly described species was Diarsia zillii Gyulai & Saldaitis, 2019, based on a single male specimen. Since, a further male and a female were found in the collected stored material, almost from the same locality and date. The genitalia dissection confirmed that the description was necessary and correct. Thus, the colour image of the female and the short characterisation of the female genitalia are given in the present paper.

The goal of this work is to develop an encryption scheme for secure transmission for colour images over untrusted channel. A bidirectional diffusion based encryption technique for 8-bit RGB colour image is performed. The encryption process is used to protect the image in transit. Also the encryption steps will be conducted using a fixed block size of $8 \times 8$ which will enhance the robustness against cipher text only attacks. The performance analysis of encryption scheme is conducted using various tests and observed that the proposed algorithm is robust against various cryptanalytic attacks.

One of the common problems observed in medicines is hepatotoxicity as liver play mainly role in metabolizes the herbal medicines. Although, the acceptance of herbal medicines is growing nowadays still there is an absence of knowledge about their toxicological properties and the right use being a hepatotoxic.This paper presents method to detect and diagnoses liver lesions in four types: necrotic cells, fatty degenerative cells, hepatocellular hypertrophic cells and congested cells using image processing techniques. The method is proposed to perform two tasks the first is conclude whether the liver image is normal or abnormal the second if abnormal state is detected then diagnosis lesions type must performs. The method progresses in many steps are preprocessing, features extraction, classification and lesion diagnosing. Grey level co-occurrence Matrix (GLCM) technique is utilize to concentrate features to distinguish between normal and abnormal case using neural network classifier if abnormal state is detected the method feedback with colour image to analyse cells shape and image intensity colour to determine which type of diseases founded in image based on statistical and morphological features of cells. The method tested on 107 images it is got on the accuracy 100% in classification and 95% in diagnosing.

In this study, we propose a new optical colour image encryption technique using spiral phase transform and chaotic pixel scrambling. For encryption, three channels of the colour image i.e. red, gre...

This study was performed to estimate choroidal thickness by fundus photography, based on image processing and deep learning. Colour fundus photography and central choroidal thickness examinations were performed in 200 normal eyes and 200 eyes with central serous chorioretinopathy (CSC). Choroidal thickness under the fovea was measured using optical coherence tomography images. The adaptive binarisation method was used to delineate choroidal vessels within colour fundus photographs. Correlation coefficients were calculated between the choroidal vascular density (defined as the choroidal vasculature appearance index of the binarisation image) and choroidal thickness. The correlations between choroidal vasculature appearance index and choroidal thickness were −0.60 for normal eyes (p

Colour vision deficiency (CVD) is a genetic condition that has troubled people for a long time. This study proposes an improved colour-to-grey method for CVD using image segmentation and a colour difference model. In this method, the colour image is first segmented using a region growing method so that each region corresponds to one colour. Next, the colour difference is computed between arbitrary segmented region pairs. Finally, the greyscale image is obtained by minimising a target function. Experimental results show that compared with state-of-the-art colour-to-grey methods, the proposed algorithm can improve the E-score by about 10.99%.

Content-based image retrieval (CBIR) is a research hotspot. To improve the performance of a CBIR system, especially the retrieval accuracy, this work proposes a method that uses a soft hypergraph combined with a weighted adjacent structure (WAS) to retrieve images. In this method, the similarities between images are computed and a similarity matrix is constructed by a conjoined colour difference histogram and micro-structure descriptor method. Furthermore, a novel WAS and a soft hypergraph model are utilised to further improve the retrieval precision. The proposed method is compared with other methods in several datasets. Experimental results manifest the performance and robustness of this proposed method.

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue-saturation-intensity (HSI) and HS-value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red-green-blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.

Weighing in at 4.2 kg (according to my kitchen scales), Murray Bird and Greg Parker’s More of the Kangaroo is a more than 1,000 page tome. With glossy pages, thick cover, and plentiful colour image...

يدرس هذا البحث¨ جماليّة تشكيل الصورة اللونية، في شعر الشاعرة الأردنية لينا أبو بكر، ارتأت الباحثة أن ترصد الصور اللونية في هذه الدراسة في شعر الشاعرة لينا أبو بكر لتعرف مدى تأثره الشاعرة بالألوان عن طريق دراسة الدلالات اللونية في شعرها. انصبّ اهتمام البحث على براعة الشاعرة في استخدام الألوان ودلالاتها والتركيز على دور الحواس في بناء الصورة، لاستثمارها في تشكيلها، من خلال توظيفها تقنيات عديدة منها: التشكيل الاستعاري الذي تشكل عبر محاولة الشاعرة التحليق في آفاق المجاز البعيدة، ولعاً بالإغراب. وتأتي أهمية دراسة الصورة اللونية في شعر لينا أبو بكر، لأنها تبوح بما يدور في نفس الشاعرة وأفكارها؛ إذ تمثل الصورة اللونية جزء من الصورة البصرية؛ وذلك لأن اللون مدرك حسّي بصري. وكان من نتائج البحث الاطمئنان إلى أن اللون في صور الشاعرة، على وضوح دلالته غالباً ما يوحي بالعمق؛ إذ يغور في أعماق الصورة ليلمس آثار تجارب الشاعرة الذاتية، وقد لمس البحث أن الصور التي وشحت قصائدها كانت مرسومة غالباً بالألوان المستعارة وبالألوان المستوحاة من دلالة المفردات والصيغ الانزياحية التي تعتمد المجاز وأضربه كالاستعارة والتشبيه وما إليها، وقد لمس البحث أيضاً تعدد دلالات الألوان في شعرها وتنوعها حسب ورودها في السياق. الكلمات المفتاحية: الصورة – اللون- الدلالة- الاستعارة - المجاز.

To reduce dependence on the subjective experiences of designers, a colour combination design method for human-machine interfaces was proposed based on colour images, gray clustering and a hierarchical interactive genetic algorithm. First, colour samples were divided into groups, and the representative colours were selected from each group using the Jiro Kawakita (KJ) method. The relationship between the unicolour samples and the semantic adjectives ‘dynamic – calm’ was investigated using the semantic differential method. Second, the gray clustering method was used to predict the tendency of the complete colour image of the human-machine interface with multiple colours. Then, a colour image positioning value was used to obtain colour combinations, and a colour database of the feasible solutions was established. Next, a hierarchical search was used to reduce the search space and to accelerate convergence in the population. The users’ interactive evaluation results were taken as an evolution conditio...

Summary Life scientists often desire to display the signal from two different molecular probes as a single colour image, so as to convey information about the probes’ relative concentrations as well as their spatial corelationship. Traditionally, such colour images are created through a merge display, where each greyscale signal is assigned to different channels of an RGB colour image. However, human perception of colour and greyscale intensity is not equivalent. Thus, a merged image display conveys to the typical viewer only a subset of the absolute and relative intensity information present in and between two greyscale images. The Commission Internationale de l'Eclairage L*a*b* colour space (CIELAB) has been designed to specify colours according to the perceptually defined quantities of hue (perceived colour) and luminosity (perceived brightness). Here, we use the CIELAB colour space to encode two dimensions of information about two greyscale images within these two perceptual dimensions of a single colour image. We term our method a Perceptually Uniform Projection display and show using biological image examples how these displays convey more information about two greyscale signals than comparable RGB colour space-based techniques.

Image processing is the process that turns an image from a science image to something more pleasing to the eye or highlights a structure of interest. Many image processing techniques render the image unsuitable for photometry. In this chapter, we discuss image processing techniques such as stacking, sigma clipping, drizzling, stretching, as well as the application of image processing filters. We touch on how to make colour images from black and white astronomical images using commercial packages.

Human re-rendering from a single image is a starkly under-constrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealistic distortions of the body parts and garments, or implausible changes of the texture. To address these challenges, we propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint, given one input image. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image and easily reposed. Instead of a colour-based UV texture map, our approach further employs a learned high-dimensional UV feature map to encode appearance. This rich implicit representation captures detailed appearance variation across poses, viewpoints, person identities and clothing styles better than learned colour texture maps. The body model with the rendered feature maps is fed through a neural image-translation network that creates the final rendered colour image. The above components are combined in an end-to-end-trained neural network architecture that takes as input a source person image, and images of the parametric body model in the source pose and desired target pose. Experimental evaluation demonstrates that our approach produces higher quality single image re-rendering results than existing methods., Published in ECCV 2020

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known. © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

The presence of sediments, lighting inconsistencies, colour variations and dissolved particles give hazy effect to the underwater images. To overcome this, enhancement techniques are required. In this paper, the model based dehazing algorithms are analysed. The effectiveness and limitations of various algorithms are analysed both in terms of subjective and objective measures. The Underwater Hazeline Prior (UHP) algorithm is contrast adjusted to form the Modified Colour Restoration (ColR M ). The ColR M achieves a metric improvement of 2.5% in Underwater Colour Image Quality Enhancement (UCIQE) than the UHP algorithm. Further contrast improvement strategies for underwater images are also discussed.

This article proposes a novel $k$ out of $n$ visual cryptography procedure using secret sharing techniques for Galois Fields. This method can be applied to share a gray-scale or a colour image between $n$ participants such that any $k$ out of them should be able to perfectly recover the shared undisclosed image unaccompanied by any distortion or loss of contrast.

In this paper, a colour image encryption method is proposed on the basis of genetic operators and generalized Henon chaotic mapping. Firstly, the initial values of Henon chaotic mapping are generated based on the plain image. Then, genetic operators are applied for image encryption, which include three operations of selection, crossover, and mutation. The selection operation is performed on the bit-level, which can eliminate the strong correlations between the pixels. The cipher image is finally obtained after crossover and mutation operations. Experimental results show that the proposed method has a large key space, high information entropy, and strong robustness against chosen-plaintext attack, which can be applied for the secure applications.