An Efficient Image Denoising Method for Wireless Multimedia Sensor Networks Based on DT-CWT

Sammouda, Rachid; Al-Salman, AbdulMalik S.; Gumaei, Abdu; Tagoug, Nejmeddine

doi:10.1155/2015/632568

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…MSE is a cumulative value of squared errors between an original image (O) and a denoised image (D) with 2D matrices with m rows and n columns. MSE has a small value if the method performs well and can be computed as [23]:…”

Section: B Image Quality Evaluation Measuresmentioning

confidence: 99%

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A Repeated Median Filtering Method for Denoising Mammogram Images

AlSalman¹

2020

IJACSA

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In the medical field, mammogram analysis is one of the most important breast cancer detection procedures and early diagnosis. During the image acquisition process of mammograms, the acquired images may be contained some noises due to the change of illumination and sensor error. Hence, it is necessary to remove these noises without affecting the edges and fine details, achieving an effective diagnosis of beast images. In this work, a repeated median filtering method is proposed for denoising digital mammogram images. A number of experiments are conducted on a dataset of different mammogram images to evaluate the proposed method using a set of image quality metrics. Experimental results are reported by computing the image quality metrics between the original clean images and denoised images that are corrupted by different levels of simulated speckle noise as well as salt and paper noise. Evaluation quality metrics showed that the repeated median filter method achieves a higher result than the related traditional median filter method.

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Section: B Image Quality Evaluation Measuresmentioning

confidence: 99%

“…The second measure is the PSNR that can give a good indication of the capability of the method to remove the noises. The small value of PSNR for the denoised image means it has a poor quality [23]. PSNR can be calculated as in the following equation.…”

Section: B Image Quality Evaluation Measuresmentioning

confidence: 99%

A Repeated Median Filtering Method for Denoising Mammogram Images

AlSalman¹

2020

IJACSA

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“…Samples are trained for learning [25,26], and then the classifier is used to identify candidate targets to obtain target location information [27], which breaks the conventional improvement algorithm that builds accurate and complex target models to improve tracking accuracy. Instead of focusing on the construction of stable and efficient classifiers to improve the performance of target recognition and tracking algorithms [28,29], which also inject new ideas into the research direction of target recognition and tracking, this method requires, with a large number of samples as support, obtaining an efficient classifier, and a very large amount of data is required [30,31]. No matter how the target tracking algorithm is improved, real time, accuracy, and stability are always the three important indicators to measure the effect of moving target tracking [32,33].…”

Section: Introductionmentioning

confidence: 99%

Using Adaptive Object Model to Basketball Tracking Algorithm and Simulation

et al. 2020

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The adaptive object model method is an effective way to develop dynamic and configurable adaptive software. It has the characteristics of metamodel, description drive, and runtime reflection. First, the core idea of the adaptive object model is explained; then, the five modes of establishing the metamodel in the adaptive object model architecture, the model engine, and supporting tools are analyzed; and the basketball tracking algorithm of the adaptive object model is discussed. Secondly, a two-dimensional joint information strategy is proposed to improve the tracking effect. When the basketball is in a very complex environment, there will always be some color information in the background that is the same as the target, which affects the effect of basketball tracking. Therefore, this paper proposes a Camshift tracking method based on the significance of histograms, through real time. The basketball movement is compared with the background histogram to continuously adjust the basketball movement tracking model. These two methods can better establish the tracking model of the basketball adaptive object, reduce the interference of background information, and achieve the effect of stable tracking of the target. The simulation experiment results show that the method proposed in this paper can effectively improve the accuracy of the basketball goal model and achieve stable tracking of the goal.

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“…The experimental results in [16] show that the DT-CWT technique could restore images with a higher peak signal-to-noise ratio (SNR) and lower Mean Square Error (MSE) than the other two methods. The system consists of two parallel Discrete Wavelet Transforms utilizing isolated low-pass and high-pass filters [17], referred to as real-tree and imaginary-tree, respectively. Additionally, it generates an analytic signal based on the fundamentals of Fourier transforms.…”

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confidence: 99%

Fast and Compact Model for Palm Bunch Classification Using DT-CWT and LSTM on Hue Histogram

Pipitsunthonsan,

Khaorapapong,

Nakasathien

et al. 2023

IEEE Access

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Traditionally, the grading process of fresh palm bunches has used human observation at palm trading sites, which is not precise and has low reliability due to human bias. Several studies have presented automatic grading methods based on image processing and machine learning. Unfortunately, these models are not sufficiently compact to be implemented on trading sites. Therefore, a compact grading model for automatic grading suitable for implementation at palm trading sites is presented. Two key elements make the model more compact and efficient. The first element is a reduction in the size of the input dataset. We achieved this by replacing the multi-dimension RGB palm bunch image with a one-dimensional hue histogram. The second element was the core engine of the proposed classifier. It consists of a dual-tree complex wavelet transform (DT-CWT) connected to the LSTM back end as the front end. The proposed model was proven using real image datasets of 800 palm bunches of several varieties collected from trading sites. The robustness of the model was investigated by verifying its accuracy in several noisy environments. Based on the testing process, the proposed model requires 0.07 second for grading and achieves 91.57% accuracy at 6 dB signal-to-noise ratio (SNR).

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An Efficient Image Denoising Method for Wireless Multimedia Sensor Networks Based on DT-CWT

Cited by 7 publications

References 46 publications

A Repeated Median Filtering Method for Denoising Mammogram Images

A Repeated Median Filtering Method for Denoising Mammogram Images

Using Adaptive Object Model to Basketball Tracking Algorithm and Simulation

Fast and Compact Model for Palm Bunch Classification Using DT-CWT and LSTM on Hue Histogram

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