Study the Quality of Image Enhancement by using Retinex Technique which Capture by different Lighting (Sun and Tungsten)

Al-Zuky, Ali A.; Salman, Salema S.; Al-Saleh, Anwar H.

doi:10.5120/12773-9862

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Compared with the photos collected under sufficient lighting conditions, whose illuminance is 10826 lux, the remote sensing images of diseased trees collected under insufficient lighting conditions contain a large amount of noise. The visibility of objects such as diseased trees, houses, and roads is poor, resulting in blurred targets and severe distortion of details ( Zuky et al., 2013 ).…”

Section: Experimental Parameters and Yolo V4 Network Structurementioning

confidence: 99%

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

Zhang,

Han,

Wang

et al. 2024

Front. Plant Sci.

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IntroductionPine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.MethodsTo address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.ResultsThe ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.DiscussionComparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease.

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Section: Experimental Parameters and Yolo V4 Network Structurementioning

confidence: 99%

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

Zhang,

Han,

Wang

et al. 2024

Front. Plant Sci.

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“…Quality of image (IQ) is usually assessed using image quality metrics such as SSIM, HVS, MSSIM and FSIM along with PSNR and MSE but unfortunately, most of used metrics cannot describe the visual quality of the enhanced digital image adequately. Even though a number of image enhancement techniques are available, development of a quantitative enhancement measure suitable for all types of images, is still a challenging area and newer metrics are being thought of every day [93,95]. In fact, there is no universal measure, which specifies both the objective and subjective validity of the enhancement for all types of images [93].…”

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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“…Even though a number of image enhancement techniques are available, development of a quantitative enhancement measure suitable for all types of images, is still a challenging area and newer metrics are being thought of every day [6] [16].…”

Section: Introductionmentioning

confidence: 99%

IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements

L¹,

Gopikakumari²

2013

IJCA

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Evaluation of images, after processing, is an important step for determining how well the images are being processed. Quality of image is usually assessed using image quality metrics. Unfortunately, most of the commonly used metrics cannot adequately describe the visual quality of the enhanced image. There is no universal measure, which specifies both the objective and subjective validity of the enhancement for all types of images. This paper is a study of the various quantitative metrics for enhancement against changes in contrast and sharpness of both general and medical images. A new metric is proposed that is useful for measuring the improvement in contrast as well as sharpness. It is computationally simple and can be used for all types of images.

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Study the Quality of Image Enhancement by using Retinex Technique which Capture by different Lighting (Sun and Tungsten)

Cited by 3 publications

References 3 publications

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements

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