Improvement of PSNR in Image Denoising by using DWT Algorithm

Gautam, Sanchesh; Amhia, Hemant

doi:10.1109/iccsp.2019.8698072

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…There are a lot of image processing approaches to enhance quality of images and videos [32,[86][87][88][89][90] but at the first, its metric should be defined. Now, well-known metrics for assessing the quality of images and videos are VQM (Video quality measurement), PVQM (Perceptual video quality measure), MPQM (Moving picture quality metrics), SSIM (Structural Similarity Index), MSSIM (Mean SSIM), FSIM (feature-similarity), PSNR (Maximum signal to noise ratio), and HVS (human visual system) [85,[91][92][93]. Human Visual System (HVS) based techniques for IQ assessments (IQA) take benefit of some known characteristics of Human Visual System in order to incorporate perceptual image quality metrics (IQM).…”

Section: Performance Evaluation Metric For the Proposed Image Transmimentioning

confidence: 99%

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

2019

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Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

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