Noise refers to the random variation of intensity of a pixel, which modifies the actual information of the image. As a result, pixels which appear in the image are not the actual pixels. Addition of extraneous values to the image causes the occurrence of noise. Noise is categorized into impulse (salt-and-pepper) noise, uniform noise, Gaussian noise, exponential noise, Erlang (gamma) noise, photon noise, speckle noise, etc. Speckle noise is the noise that arises due to the effect of environmental conditions on the imaging sensor during image acquisition. Speckle noise is mostly detected in case of medical images, active Radar images and Synthetic Aperture Radar (SAR) images. Various researchers have performed experiments to overcome this kind of noise using different filtering techniques based on soft computing approaches, such as Fuzzy Filter, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Neural Networks, etc. In this paper, we present a brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner.

A Comparative Study on Approaches to Speckle Noise Reduction in Images

Passive forensics in image and video using noise features

Image enhancement is considered as one of the most important techniques in image research. The main aim of image enhancement is to enhance the quality and visual appearance of an image, or to provide a better transform representation for future automated image processing. Many images like medical images, satellite, aerial images and also real life photographs suffer from poor and bad contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement Techniques. It improves the clarity of images for human viewing, removing blurring and noise, increasing contrast, and revealing details. These are examples of enhancement operations. The enhancement technique differs from one field to another depending on its objective. The existing techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency Domain Enhancement. In this paper, we present an overview of Image Enhancement Processing Techniques in Spatial Domain. More specifically, we categorise processing methods based representative techniques of Image enhancement. Thus the contribution of this paper is to classify and review Image Enhancement Processing Techniques as well as various noises has been applied to the image. Also we applied various filters to identify which filter is efficient in removing particular noises. This is identified by comparing the values obtained in PSNR and MSE values. From this we can get an idea about which filters is best for removing which types of noises. It will be useful and easier to detect the filters for future research.

Image Enhancement Techniques: A Study

In this article, a detailed survey of the quantum approach to image processing is presented. Recently, it has been established that existing quantum algorithms are applicable to image processing tasks allowing quantum informational models of classical image processing. However, efforts continue in identifying the diversity of its applicability in various image processing domains. Here, in addition to reviewing some of the critical image processing applications that quantum mechanics have targeted, such as denoising, edge detection, image storage, retrieval, and compression, this study will also highlight the complexities in transitioning from the classical to the quantum domain. This article shall establish theoretical fundamentals, analyze performance and evaluation, draw key statistical evidence to support claims, and provide recommendations based on published literature mostly during the period from 2010 to 2015.

Advances in the Quantum Theoretical Approach to Image Processing Applications

The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.

Automatic recognition of handwritten Arabic characters: a comprehensive review

Getting an efficient method of removing noise from the images, before processing them for further analysis is a great challenge for the researchers. Noise can degrade the image at the time of capturing or transmission of the image. Before applying image processing tools to an image, noise removal from the images is done at highest priority. Ample algorithms are available, but they have their own assumptions, merits and demerits. The kind of the noise removal algorithms to remove the noise depends on the type of noise present in the image. Best results are obtained if testing image model follows the assumptions and fail otherwise. In this paper, light is thrown on some important type of noise and a comparative analysis of noise removal techniques is done. This paper presents the results of applying different noise types to an image model and investigates the results of applying various noise reduction techniques.

A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques

Reputation systems are an important means to facilitate trustworthy interactions between on-and off-chain services and users. However, contemporary reputation systems are typically dependent on a trusted central authority to preserve privacy of raters or on adding noise into the user feedback. Moreover, the accuracy of reputation values relies on the integrity of user feedback or input; this feedback should not be tampered with or misused for other purposes. This paper presents blockchain-based reputation system named REPUTABLE (A Decentralized Reputation System for blockchain-based Ecosystems), which computes the reputation of service providers and external services within a blockchain ecosystem through decentralized on-chain and off-chain implementation. Specifically, REPUTABLE not only ensures privacy, but also reliability, integrity and accuracy of reputation values, while incurring minimal overhead. It also enables performing certain data or statistical analytics functions on user feedback, whilst preserving security, privacy, accountability and unlinkability of participants and their feedback. We present a proof-of-concept implementation and a demonstration of the REPUTABLE system. Finally, by means of formal and empirical evaluation, we show the effectiveness of our proposed system to preserve the anonymity of user feedback and the high performance of its blockchain-based implementation.

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REPUTABLE–A Decentralized Reputation System for Blockchain-Based Ecosystems

Wireless Sensor Network (WSN) is the network of minimal expensive and tiny devices for sensing, which is known as sensors. These networks are vulnerable to various types of attacks namely black hole attack, Sybil attack and so forth. Black hole attack is the most harmful attack and also the access way for all the other types of attacks, whereas Sybil attack is a dangerous hazard to sensor networks in which sensor nodes have numerous identities. These attacks may interrupt the performance and functioning of network, which may cause harm to the network system if not properly detected. Hence, an efficacious technique is proposed for black hole and Sybil attack detection utilizing Adaptive Taylor Sail fish optimizer (Adaptive Taylor-SFO) algorithm. Firstly, WSN nodes are simulated in a network and thereafter, routing is executed by Adaptive Taylor-SFO, which is newly designed by an integration of Adaptive concept with Taylor series and Sail Fish optimizer (SFO) for selecting an optimal route by consideration of fitness measures namely, delay, energy, and distance. Finally, detection of black hole and Sybil attack is carried out by Deep stacked auto encoder. Moreover, the network classifier is tuned employing proposed Adaptive Taylor-SFO. Thus, the proposed technique effectually classified into normal, black hole and Sybil attack. Additionally, proposed technique obtained minimal delay of 19.91ms, FNR of 0.0773, FPR of 0.1026, maximal throughput of 125.73kbps and maximal PDR of 97.52%.

Adaptive Taylor-Sail Fish Optimization based deep Learning for Detection of Black Hole and Sybil Attack in Wireless Sensor Network

Sensor nodes in Wireless sensor network (WSN) are distributed over a large area for sensing the pressure, temperature, humidity, and so on. They are at risk due to several attacks. In an attack like a black hole, the malicious node captures the whole data without any consideration of the active route, thus the source node are secured for communication. Hence, a new method name, Taylor SailFish Optimizer (TaylorSFO) is proposed to predict blackhole attacks in WSN. The training of the Deep stacked autoencoder is done through proposed Taylor-SFO, which is the integration of Taylor Series, and SailFish Optimizer (SFO). The newly developed Taylor-SFO is then applied for routing and blackhole attack detection at the WSN base station. Overall, two phases are included in the proposed model, which involves routing and blackhole attack detection at the base station. Initially, the WSN nodes are given to the routing module. Here, the routing is done based on the proposed TaylorSFO. Energy, distance as well as delay are the three fitness parameters considered for the routing. The proposed method shows the lowest delay of 21.23 ms, minimal FNR of 0.083, minimal FPR of 0.134, highest PDR of 94.87%, the highest throughput rate of 119.98 kbps, respectively.

Jahid Ali

Papers

A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques

REPUTABLE–A Decentralized Reputation System for Blockchain-Based Ecosystems

Adaptive Taylor-Sail Fish Optimization based deep Learning for Detection of Black Hole and Sybil Attack in Wireless Sensor Network

Taylor Sailfish Optimizer-Based Deep Stacked Auto Encoder for Blackhole Attack Detection in Wireless Sensor Network

TruCert: Blockchain-based trustworthy product certification within autonomous automotive supply chains