Search results

The purpose of this paper is to construct a novel grey filter model for image denoising and to solve the problems which exist in the image denoising filter method, in which the true intensity value of each noisy pixel cannot be predicted better.

Based on the definition of stepwise, the defects of traditional grey prediction models are found. A new grey filter model, named grey stepwise prediction model, is proposed. The new filter model for the image denoising is based on each noisy pixel's neighborhoods stepwise, which is the eight pixels around the noisy pixel, to predict its intensity value and to solve the problems which exist in the image denoising filter method.

The experiment results show that the improved filter model can effectively eliminate image noise, preserve the image's details and edges, increase SNR (signal‐to‐noise ratio) as well as PSNR (peak signal‐to‐noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect.

The new filter method exposed in the paper can be used to 8‐bit gray‐scale image denoising. The method can also be used to binary image denoising.

The paper succeeds in constructing a novel filter method for image denoding, and it is undoubtedly a new development in image recovery algorithm and grey systems theory.

The technique for hiding confidential data in specific digital media by enhancing the graphical contents is known as watermarking. The dissemination of information over a secure channel is essential for multimedia applications. The purpose of this study is to develop a secure communication approach for OFDM system.

This paper exploits a secure communication in the orthogonal frequency division multiplexing (OFDM) system using wavelet-based video watermarking technique. In this work, the Chronological-MS algorithm is used for securing the data communication in the OFDM system. Here, the secret message is embedded in video frames using wavelet transform for hiding sensitive information and the hidden information is transmitted over the OFDM system. The Chronological-MS algorithm is used for selecting the optimal regions in the video for embedding secret message. In embedding phase, wavelet coefficients are obtained by applying wavelet transform on the frame for embedding the secret message. Meanwhile, in extraction phase, the inverse wavelet transform is applied to extract the secret message.

Considering number of frames, the maximum Peak signal-to-noise ratio (PSNR) value is attained by proposed Wavelet + Chronological MS method for Video 2 with value 73.643 dB, respectively. Meanwhile, the minimum mean squared error (MSE) attained by the proposed Wavelet + Chronological MS method is when considering number of frames with MSE values as 0.001 for both Videos 1 and 2. The minimum bit error rate (BER) value is attained by the proposed method with value 0.00009 considering random noise with Video 1. Thus, the proposed Wavelet + Chronological MS have shown better results than the existing techniques.

This work proposes a wavelet-based watermarking method using Chronological-MS, for initiating secured communication over an OFDM. One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. Hence, the proposed method offers the highly secured data transmission over the OFDM.

The purpose of this paper is to develop an innovative information hiding algorithm.

The proposed algorithm is based on image histogram statistics. Cumulative‐peak histogram regions are utilized to hide multiple bits of the secret message by performing histogram bin substitution. The embedding capacity, otherwise known as payload, and peak signal to noise ratio (PSNR), as well as security, are the main metrics used to evaluate the performance of the proposed algorithm.

According to the obtained results, the proposed algorithm shows high embedding capacity and security at comparable PSNR compared with existing hiding information techniques.

The simplicity, security, random distribution of embedding pixels, and on‐demand high capacity are the key advantages of the proposed approach.

The purpose of this paper is to develop an innovative information hiding algorithm.

The proposed algorithm is based on image histogram statistics. Cumulative‐peak histogram regions are utilized to hide multiple bits of the secret message by performing histogram bin substitution. The embedding capacity, otherwise known as payload, and peak signal to noise ratio (PSNR), as well as security, are the main metrics used to evaluate the performance of the proposed algorithm.

According to the obtained results, the proposed algorithm shows high embedding capacity and security at comparable PSNR compared with existing hiding information techniques.

The simplicity, security, random distribution of embedding pixels, and on‐demand high capacity are the key advantages of the proposed approach.

The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost.

Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters.

Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k).

This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery.

According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.

The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.

The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.

Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

Video transmission effectiveness in the Ad Hoc network is becoming important recently, if different routing protocols are applied. Some researchers conclude that the reactive protocols are better for file transfer protocol (FTP) and constant bit rate (CBR) or hypertext transfer protocol (HTTP) transmission in an Ad Hoc wireless network but the performance report of video transmission is not much. This study adopts Qualnet (Network Simulator) as a simulation tool for environmental designing and performance testing, and employs an experimental design with eight groups. Our experiment shows that: (1) The performance of AODV (reactive) protocol is better than DSDV, ZRP and DSR when the transmission load has only one video stream; (2) Proactive (DSDV) and Hybrid protocols (ZRP) are better for a smaller Ad Hoc network when it transmits a video stream with some applications (VoIP, FTP and CBR). We conclude that packet loss rate is sensitive to the quality of video transmission and it has negative relationship with Peak Signal‐to‐Noise Ratio (PSNR) value. In addition, our experiment also shows that PSNR is a simple Metric for the performance evaluation of video transmission.

This paper aims to present an inexact 4:2 compressor cell using carbon nanotube filed effect transistors (CNFETs).

To design this cell, the capacitive threshold logic (CTL) has been used.

To evaluate the proposed cell, comprehensive simulations are carried out at two levels of the circuit and image processing. At the circuit level, the HSPICE software has been used and the power consumption, delay, and power-delay product are calculated. Also, the power-delaytransistor count product (PDAP) is used to make a compromise between all metrics. On the other hand, the Monte Carlo analysis has been used to scrutinize the robustness of the proposed cell against the variations in the manufacturing process. The results of simulations at this level of abstraction indicate the superiority of the proposed cell to other circuits. At the application level, the MATLAB software is also used to evaluate the peak signal-to-noise ratio (PSNR) figure of merit. At this level, the two primary images are multiplied by a multiplier circuit consisting of 4:2 compressors. The results of this simulation also show the superiority of the proposed cell to others.

This cell significantly reduces the number of transistors and only consists of NOT gates.

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.