Images are corrupted by the Additive White Gaussian Noise (AWGN) from imaging devices. Existing spatial-domain filters often introduce edge distortion at high noise levels. We propose the Inverse Gaussian Harmonic Filter (IGHF), a novel training-free denoising framework that inverts the Gaussian kernel and employs harmonic weighting on standard deviation terms. The first closed-form mathematical model provides superior edge preservation while effectively suppressing noise. We evaluate hybrid approaches combining IGHF with BM3D [K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. 8 (2007) 2080–2095.] and DnCNN [K. Zhang K, W. Zuo W, Y. Chen, D. Meng and L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process. 7 (2017) 3142–3155.], achieving up to 1.50[Formula: see text]dB PSNR gain over the state-of-the-art spatial methods. Quantitative evaluation on eight benchmark images demonstrates effectiveness across noise levels ([Formula: see text]–0.05). Triple-Hybrid fusion achieves 31.96[Formula: see text]dB PSNR with superior SSIM (0.912) and edge-preservation FOM (0.850), outperforming BM3D by 0.35[Formula: see text]dB through systematic grid-search optimization. Runtime analysis confirms real-time capability: Enhanced IGHF processes HD frames in [Formula: see text] [Formula: see text]ms with minimal memory footprint ([Formula: see text] [Formula: see text]MB), enabling consumer-grade deployment. The deterministic approach provides a novel mathematical foundation for spatial-domain denoising with applications in deep-learning architectures.

ABSTRACT Increasing data transmission volumes are causing more frequent and more severe network congestion. In order to handle spikes in network traffic, a substantially bigger buffer has been included into the system. Bufferbloat, which happens when a bigger buffer is implemented, exacerbates network congestion. Using the transfer control protocol (TCP) congestion management strategy with active queue management (AQM) can fix this issue. As congestion increases, it becomes increasingly difficult to forecast and fine‐tune dynamic AQM/TCP systems in order to achieve acceptable performance. To shed new light on the AQM system, we plan to use deep reinforcement learning (DRL) techniques. It is possible that AQM can learn about the appropriate drop policy the same way people do when using a model‐free technique like DRL‐AQM. After training in a simple network scenario, DRL‐AQM is able to recognize complex patterns in the data traffic model and apply them to improve performance in a wide variety of scenarios. Offline training precedes deployment in our approach. In many cases, the model does not require any further parameter tweaks after training. Even in the most complicated networks, AQM algorithms have proven to be effective, regardless of the network's complexity. Minimizing buffer capacity use is an important goal of DRL‐AQM. It automatically and continually adjusts to changes in network connectivity.

Many different histogram equalization (HE)-based image enhancement methods have been developed to overcome the problems of low or high image brightness, contrast sensitivity, and difficulty in revealing details of dark areas under low-light environments. In this paper, a novel image enhancement method based on HE and adaptive gamma correction with weight distribution (AGCWD) is proposed for natural and effective image enhancement. In this method, histogram stretching is performed on Red–Green–Blue (RGB) color components of image, and then the color space of RGB image is converted to Hue–Saturation–Intensity (HSI) color space. The histograms of S and I components are divided into sub-histograms according to the exposure threshold. The underexposure regions are enhanced with a new AGCWD. Then, the color space of HSI image is converted to RGB color space. Finally, the HE is applied to the input image with the obtained image histogram map. Thus, the method has not only effectively prevented the over-enhancement of the contrast but also obtained the quality and natural enhanced image. The proposed method is compared with the most known contrast enhancement methods and low-light enhancement methods. Experimental results have supported that the proposed method outperforms other methods in terms of both visual perception and objective evaluation.

Abstract In recent years, the protection of the intellectual property rights of digital media has become a considerable issue. Especially using digital image editing tools has made it very easy and fast to manipulate images. Therefore, manipulation detection of digital images has become a necessity. This article presents a novel Frei‐Chen based self‐embedding fragile watermarking technique implemented to blocks for image manipulate detection. The block key and the Frei‐Chen based block feature are calculated from the non‐overlapping image blocks of size . Exclusive‐OR operations are applied to the Frei‐Chen based block feature and the block key. After, a nine bit block authentication key is generated from the Frei‐Chen based block feature and the block key. Then the generated authentication key is embedded in the least significant bits of each block. Experimental results prove the effectiveness of the proposed method through the detection of the various attacks and obtain the excellent visual quality of watermarked images. Also, the results demonstrate that the proposed method detects all of the manipulated areas plainly.

Summary Copyright protection of digital images has become a consequential issue in recent years. To prevent a breach of copyright for a color image, this article presents a novel discrete wavelet transform (DWT) based robust image watermarking method with a color channel multiplexing approach. In this proposed method, the original color image is separated into red, green, and blue color channels. One of these color channels is copied and a set of four channels is obtained. Then these channels are combined to obtain a new gray‐level cover image. 2‐level DWT is performed on the cover image. Arnold scrambled binary watermark image i embedded into the low‐frequency sub‐band of the cover image. 2‐level inverse DWT is performed to produce a gray‐level watermarked image. Then a demultiplexing process is performed by separating the gray‐level watermarked image into four color channels. The average values of the two copied color channels are calculated and then converted into a single‐color channel. Finally, the watermarked color image is get by merging three different color channels. To approve the performance of the presented method, various experimental tests have been performed and a benchmarking analysis has been conducted. The results show that the proposed method is robust against non‐linear and linear attacks while protecting watermarking imperceptibility.

In this paper, we investigate the capacity limits in an variable duty cycle VANET that uses the IEEE 802.11p standard. We show that the default value of duty cycle that allocates time equally between control and service channels (CCH and SCH, respectively) leads to unbalanced performance, and that the performance of typical traffic on those channels can be greatly improved by judicious choice of the value for duty cycle. We also find the values of spatial density of vehicular traffic that lead to spatial saturation of network traffic.

ABSTRACT In this paper, we investigate the capacity limits in a variable duty cycle VANET that uses the IEEE 802.11p standard. We show that the default value of duty cycle that allocates time equally between control and service channels leads to unbalanced performance and that the performance of typical traffic on those channels can be greatly improved by judicious choice of the value for duty cycle. We find the values of spatial density of vehicular traffic that lead to spatial saturation of network traffic. In addition, to model the distribution of the vehicles on the road segment, we used Poisson distribution, as well as Erlang‐k distribution. Copyright © 2012 John Wiley & Sons, Ltd.

IEEE 802.11p is a new communication standard for wireless access in vehicular environment (WAVE), intended to support road safety messages, electronic toll collection, infotainment services, and other as well as a number of other intelligent transportation systems (ITS) applications. In this paper, we focus on the impact of the dwelling time on the control channel (CCH) vs. that on the SCH (i.e., the duty cycle) on the performance of video communication on the service channel (SCH), using a novel analytical model. We show that a range of duty cycles allows for satisfactory performance of video traffic on SCH as well as timely delivery of safety messages on CCH.

In this paper, we investigate the IEEE 802.11p based Vehicular Ad-hoc Network transition between non-saturation and saturation regimes with mobile nodes. We combine vehicular traffic theory, queuing model and Markov chain to evaluate the performance of the network under spatial or networking saturation for mutiple data classes over control and service channels with different transmission rates depending on the vehicle's distance from the roadside unit (RSU). Our results show that the vehicle density growth can result in saturation of wireless medium around the RSU, further resulting in buffer overflows at on board units.

ABSTRACT We investigate the performance of simple cognitive personal area networks (CPANs) with cooperative sensing. Nodes are equipped with small buffers of capacity K each, and each node is allowed to transmit a batch of up to μ packets in one transmission cycle. Upon transmission, each node must support the operation of the CPAN by performing sensing duty in the amount obtained by multiplying the number of packets sent in a batch by a variable penalty coefficient. We model this system and show the relationship between values of design parameters and piconet performance. We also show that performance and bandwidth utilization can be improved by a simple technique of dynamically adjusting the duration of the superframe to cater to instantaneous traffic volume. Copyright © 2011 John Wiley & Sons, Ltd.

Several watermarking schemes have been proposed with the purpose of copyright protection and access control for multimedia objects. In this paper, an optimal Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) based image watermarking scheme using Particle Swarm Optimizer (PSO) is presented. Proposed DWT-SVD based watermarking algorithm initially decomposes the host image into subbands, afterwards the singular values of each subband of the host image are modified by different scaling factors to embed the watermark image. Modifications are optimized using PSO to obtain the highest possible robustness without losing the transparency. Experimental results show improvement both in transparency and robustness under certain attacks.

In this paper, a novel fragile watermarking scheme based on discrete cosine transform (DCT) using particle swarm optimization (PSO) algorithm is presented. Embedding watermarks in frequency domain can usually be achieved by modifying the least significant bits (LSBs) of the transformation coefficients. After embedding process is completed, a number of rounding errors appear due to conversion of real numbers into integers in the process of transformation of image from frequency domain to spatial domain. A population based stochastic optimization technique (PSO) is proposed to correct these rounding errors. Simulation results show the feasibility of employing PSO for watermarking and the accuracy of this novel method.