RETRACTED: A Novel approach in Hybrid Median Filtering for Denoising Medical images

Seetharaman, R.; Tharun, M.; Anandan, Kavitha

doi:10.1088/1757-899x/1187/1/012028

Cited by 7 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 6 illustrates the common noise of image system. In order to reduce the interference of image by noise, the following methods are generally used to filter noise: mean filter, median filter, adaptive filter, and wavelet transform [24]. e main method of mean filtering is the neighborhood average method, and the equation is as follows.…”

Section: E N(x Y)mentioning

confidence: 99%

Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor

Ding

Abdullah

Hamid

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This research focuses on the improvement of intelligent Hanfu design system’s performance. Henceforth, in the current study, an intelligent Hanfu design data acquisition system based on scientific graphics programming and wearable multisensor is designed for the field of human body size measurement and human model reconstruction. Initially, on the basis of wearable multisensor, a design of the recognition and analysis system of human posture is presented. The error of nine-axis inertial sensor in the process of collecting data, the data of accelerometer, gyroscope, and magnetometer are preprocessed, and the processing results are observed. Moreover, MATLAB programming is used for preprocessing of collected images. It is also used for grey normalization, filtering denoising, and image sharpening. The advantages and disadvantages of different methods are compared experimentally. Experiments show that MATLAB programming is more suitable for grey normalization than histogram equalization, and local brightness will appear in histogram equalization. After denoising the image with salt-and-pepper noise and Gaussian noise, the median filter and mean filter have defects. Wiener’s adaptive filter leads to the increase of noise, and wavelet denoising has the best effect. The data acquisition system of intelligent Hanfu design established in this work provides a certain direction for the development of intelligent Hanfu design.

show abstract

Section: E N(x Y)mentioning

confidence: 99%

Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor

Ding

Abdullah

Hamid

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The realm of image denoising has witnessed a rich history of traditional methods aimed at restoring images corrupted by various noise sources [20]. Spatial filters, including mean [21], median [22][23], and Gaussian [24][25] filters, constitute some of the foundational techniques utilized for noise suppression in images. These filters operate on the pixel-level information, smoothing out noise while preserving image structures to a certain extent [26].…”

Section: B Traditional Image Denoising Methodsmentioning

confidence: 99%

“…In [22] proposed a novel speckle noise-distorted image denoising method. They show their picture denoising research and explore thresholding methods as SureShrink, VisuShrink, and BayesShrink.…”

Section: B Traditional Image Denoising Methodsmentioning

confidence: 99%

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

Ferzo,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.

show abstract

“…Then, the water gauge image is grayed out and binarized, and the binarized water gauge image is obtained. In order to remove the noise in the binary image, Gaussian filtering is used to eliminate Gaussian noise [ 24 ], and median filtering [ 25 ] is used to eliminate impulse noise and salt and pepper noise. Since the value of each pixel of Gaussian filtering is obtained by the weighted average of itself and other pixel values in the neighborhood, the closer the distance to the object pixel, the greater the weight, the farther the distance, and the smaller the weight, and The edges of the binarized image become blurred.…”

Section: Methods Descriptionmentioning

confidence: 99%

A Water Level Measurement Approach Based on YOLOv5s

Qiao

Yang

Wang

2022

Sensors

View full text Add to dashboard Cite

Existing water gauge reading approaches based on image analysis have problems such as poor scene adaptability and weak robustness. Here, we proposed a novel water level measurement method based on deep learning (YOLOv5s, convolutional neural network) to overcome these problems. The proposed method uses the YOLOv5s to extract the water gauge area and all scale character areas in the original video image, uses image processing technology to identify the position of the water surface line, and then calculates the actual water level elevation. The proposed method is validated with a video monitoring station on a river in Beijing, and the results show that the systematic error of the proposed method is only 7.7 mm, the error is within 1 cm/the error is between 1 cm and 3 cm, and the proportion of the number of images is 95%/5% (daylight), 98%/2% (infrared lighting at night), 97%/2% (strong light), 45%/44% (transparent water body), 91%/9% (rainfall), and 90%/10% (water gauge is slightly dirty). The results demonstrate that the proposed method shows good performance in different scenes, and its effectiveness has been confirmed. At the same time, it has a strong robustness and provides a certain reference for the application of deep learning in the field of hydrological monitoring.

show abstract

RETRACTED: A Novel approach in Hybrid Median Filtering for Denoising Medical images

Cited by 7 publications

References 7 publications

Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor

Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

A Water Level Measurement Approach Based on YOLOv5s

Contact Info

Product

Resources

About