scholarly journals Image Denoising Using Multiwavelet Transform with Different Filters and Rules

2021 ◽  
Vol 15 (15) ◽  
pp. 140
Author(s):  
Muna Majeed Laftah
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Original Signal ◽  
Result Section ◽  
Switching Median Filter ◽  
Multiwavelet Transform ◽  
Denoised Image ◽  
Better Than

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</p>

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

scholarly journals ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Danang Surya Candra
Keyword(s):  
Data Fusion ◽  
Spatial Resolution ◽  
Rural Areas ◽  
Signal To Noise Ratio ◽  
Test Site ◽  
Multispectral Images ◽  
Fused Image ◽  
Pc Method ◽  
Better Than

Image fusion is a process to generate higher spatial resolution multispectral images by fusion of lower resolution multispectral images and higher resolution panchromatic images. It is used to generate not only visually appealing images but also provide detailed images to support applications in remote sensing field, including rural area. The aim of this study was to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on rural areas. GS method was compared with Principle Component Spectral Sharpening (PC) method to evaluate the reliability of GS method. In this study, the performance of GS was presented based on multispectral and panchromatic of SPOT-6 images. The spatial resolution of the multispectral (MS) image was enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and statistically. Relative Mean Difference (RMD), Relative Variation Difference (RVD), and Peak Signal to Noise Ratio (PSNR) Index were used to assess the fused image statistically. The test sites of rural areas were devided into four main areas i.e., whole area, rice field area, forest area, and settlement. Based on the results, the visual quality of the fused image using GS method was better than using PC method. The color of the fused image using GS was better and more natural than using PC. In the statistical assessment, the RMD results of both methods were similar. In the RVD results, GS method was better then PC method especially in band 1 and band 3. GS method was better than PC method in PSNR result for each test site. It was observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS was a robust method for SPOT-6 data fusion especially on rural areas.

scholarly journals Performance estimation of Adaptive threshold median filter design in image Denoising

2018 ◽  
Vol 7 (2.16) ◽  
pp. 33
Author(s):  
Shruti Bhargava Choubey ◽  
Abhishek Choubey
Keyword(s):  
Filter Design ◽  
Median Filter ◽  
Window Size ◽  
Performance Estimation ◽  
Intermediate Data ◽  
Small Window ◽  
Field Programmable ◽  
Image Details ◽  
Better Than

Picture denoising is utilized as a part of numerous fields like PC vision, remote detecting, medicinal imaging, apply autonomy and so forth. In a significant number of these applications the presence of rash clamour in the procured pictures is a standout amongst the most widely recognized issues.  The concept of this method is to provide simple but efficient method of image de-noising using filter to improve the performance and reduce the complexity of implementation. This method use the combination of average filtering and median filtering to remove the noise and produce better results with small window size 3x3. So the image details preservation is also better with small window. Mathematical results show that quality of image is better than the other filtering methods. Hardware implementation of this method is also very easy; because less number of calculations required removing the noise. Reconfigurable hardware filters may be embedded with photo acquirements provision to gain that goal. Field programmable doorway order (FPGA) is appropriate because pipelining or parallelism facts processing. What’s more, though the filtering algorithm techniques huge amount over data, however such does no longer require to shops a cluster regarding intermediate data and has the consequent properties: easy of computing or reproducible, for this reason it is suitable to be applied the usage of FPGA.  

scholarly journals Casting Blanks Cleanliness Evaluation Based on Ultrasonic Microscopy and Morphological Filtering

Metals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 796
Author(s):  
Heng Ding ◽  
Qingting Qian ◽  
Xue Li ◽  
Zhu Wang ◽  
Min Li
Keyword(s):  
Signal To Noise Ratio ◽  
New Technology ◽  
Continuous Caster ◽  
Internal Quality ◽  
Ultrasound Images ◽  
Morphological Filtering ◽  
Ultrasound Technology ◽  
Metal Materials ◽  
Better Than

The cleanliness of the casting blanks could seriously affect the quality of downstream products. Recently, ultrasound technology has been introduced to detect the inclusions in metal materials. However, due to the anisotropy of the material crystal, the ultrasonic wave has the characteristics of multiple scattering and refraction in its propagation process. This makes it difficult to evaluate the casting blanks cleanliness effectively, for the inclusion echoes are submerged in the background noise. Therefore, the ultrasonic microscope is innovatively proposed to carry out efficient scanning on the casting blanks. In the meantime, the morphological filtering algorithm has the advantages of fewer parameters and faster calculation speed which can be used to increase the signal-to-noise ratio of ultrasound images and extract the defect features more efficiently. In order to verify the effectiveness of the proposed method, specimens were taken from three strands of continuous caster for detection and analysis. The experimental results show that the second strand has the best quality and the cleanliness is 2.2/mm3, which is obviously better than the other two strands. This method will provide a new technology for the quantitative evaluation of the internal quality of the casting blanks.

Insulator Image Denoising Based on the Simplified PCNN Model

2013 ◽  
Vol 718-720 ◽  
pp. 2092-2098 ◽  
Author(s):  
Dan Li ◽  
Hong Ying He ◽  
Yi Jia Cao ◽  
Dian Sheng Luo
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Infrared Image ◽  
Window Size ◽  
Denoising Method ◽  
Similarity Calculation ◽  
Image Edge ◽  
Filter Noise ◽  
Better Than

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.

scholarly journals Selective Route Based on SNR with Cross-Layer Scheme in Wireless Ad Hoc Network

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Istikmal ◽  
Adit Kurniawan ◽  
Hendrawan
Keyword(s):  
Routing Protocol ◽  
Ad Hoc ◽  
Signal To Noise Ratio ◽  
Cross Layer ◽  
Hop Count ◽  
Routing Overhead ◽  
Fifth Generation ◽  
Distance Vector ◽  
Better Than

In this study, we developed network and throughput formulation models and proposed new method of the routing protocol algorithm with a cross-layer scheme based on signal-to-noise ratio (SNR). This method is an enhancement of routing protocol ad hoc on-demand distance vector (AODV). This proposed scheme uses selective route based on the SNR threshold in the reverse route mechanism. We developed AODV SNR-selective route (AODV SNR-SR) for a mechanism better than AODV SNR, that is, the routing protocol that used average or sum of path SNR, and also better than AODV which is hop-count-based. We also used selective reverse route based on SNR mechanism, replacing the earlier method to avoid routing overhead. The simulation results show that AODV SNR-SR outperforms AODV SNR and AODV in terms of throughput, end-to-end delay, and routing overhead. This proposed method is expected to support Device-to-Device (D2D) communications that are concerned with the quality of the channel awareness in the development of the future Fifth Generation (5G).

scholarly journals Denoising in Magnetic Resonance Images using Improved Gaussian Smoothing Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 3693-3696
Keyword(s):  
Magnetic Resonance ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Smoothing Technique ◽  
Imaging Studies ◽  
Gaussian Smoothing ◽  
Better Than

Magnetic Resonance Images (MRI) are usually prone to noise like Rician and Gaussian noise. It is very difficult to perform image processing functions with the presence of noise. The objective of our work is to investigate the best method for denoising the MRI images. This study included 25 MRI subjects selected from the Open Access Series of Imaging Studies (OASIS) - 3 database. The 25 brain image subjects includes cases of both men and women aged 60 to 80. The input RGB image is first converted to gray scale image in which the contrast, sharpness, shadow and structure of the color of image are preserved. The proposed work uses an improved Gaussian smoothing technique for denoising Magnetic Resonance Images by constructing a modified mask for Gaussian smoothing. The performance of the proposed technique has been compared with various filters like median filter, Gaussian filter and Gabor filter. The performance evaluation was carried out by metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) index. The experimental results show that the Improved Gaussian Smoothing Technique (IGST) performs better than other methods. All experiments were conducted using Scikit Learn version 0.20 and Scikit Image version 0.14.1 under Python version 3.6.7.

Efficient partial product reduction for image processing application using approximate 4:2 compressor

Circuit World ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naresh Kattekola ◽  
Amol Jawale ◽  
Pallab Kumar Nath ◽  
Shubhankar Majumdar
Keyword(s):  
Design Methodology ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Hybrid Architecture ◽  
Processing Application ◽  
Content Type ◽  
Error Metrics ◽  
Image Processing Application

Purpose This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image. Design/methodology/approach The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block. Findings Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs. Originality/value The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.

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