scholarly journals Application of Douglass-Gunn ADI Scheme on Diffusion Model with Different Noise Level for Image Denoising

2018 ◽  
Vol 7 (4.33) ◽  
pp. 10
Author(s):  
Nur Aimi Abdul Aziz ◽  
Suhaila Abd Halim
Keyword(s):  
Image Denoising ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Alternating Direction ◽  
Adi Scheme ◽  
Blurring Effect ◽  
Image Quality Index ◽  
Image Detail

Noise level is the amount of noise that corrupted the clear image in order to test on the filtering method of an algorithm proposed for image denoising. Most of the existing filtering techniques are able to remove noise but unable to preserve the image detail well and hence causing the blurring effect. Due to that, the objectives of this paper are to propose and implement Douglas-Gunn Alternating Direction Implicit (DG-ADI) on Anisotropic Diffusion (AD) model. Then, measure the performance of the proposed scheme with different level of noise. PDE based model is applied with the unconditional stable of DG-ADI scheme to remove the noise that corrupted the images. The AD model is used for preserving the image structures and edges. In this paper, a set of grayscale images from standard database is being filtered with three different noise levels in order to measure the performance of the proposed schemes. The performance of the proposed scheme is measured using the Mean Structural Similarity Index (MSSIM), Peak Signal to Noise Ratio (PSNR), Universal Image Quality Index (UIQI) and processing time. The implementation of the algorithm is completed using MATLAB R2013a. Experimental results show that the DG-ADI scheme able to remove noise with different noise level. The used of DG-ADI scheme in solving the AD model can remove the noise well without destroy the structure of image with appropriate parameters setting in grayscale image.  

A compensating total variation image denoising model combining L1 and L2 norm

2020 ◽  
pp. 002072092092330
Author(s):  
Liqiong Zhang ◽  
Min Li ◽  
Xiaohua Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Isotropic Diffusion ◽  
Model Combining ◽  
L2 Norm ◽  
Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

scholarly journals New model for local fractional integral of Chebyshev polynomials for image denoising

2019 ◽  
pp. 22-28
Author(s):  
Suzan J Obaiys ◽  
Hamid A Jalab ◽  
Rabha W Ibrahim
Keyword(s):  
Image Denoising ◽  
Chebyshev Polynomials ◽  
Fractional Integral ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Input Image ◽  
Convolution Product ◽  
Local Fractional Integral ◽  
Image Pixels

The use of local fractional calculus has increased in different applications of image processing. This study proposes a new algorithm for image denoising to remove Gaussian noise in digital images. The proposed algorithm is based on local fractional integral of Chebyshev polynomials. The proposed structures of the local fractional windows are obtained by four masks created for x and y directions. On four directions, a convolution product of the input image pixels with the local fractional mask window has been performed. The visual perception and peak signal-to-noise ratio (PSNR) with the structural similarity index (SSIM) are used as image quality measurements. The experiments proved that the accomplished filtering results are better than the Gaussian filter. Keywords: local fractional; Chebyshev polynomials; Image denoising

Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

2020 ◽  
Vol 20 (02) ◽  
pp. 2050008
Author(s):  
S. P. Raja
Keyword(s):  
Image Compression ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Set Partitioning ◽  
Complete Analysis ◽  
Structural Content ◽  
Image Quality Index

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

scholarly journals A robust watermarking algorithm for medical images

2020 ◽  
Vol 20 (3) ◽  
pp. 1601
Author(s):  
Ahmed Nagm ◽  
Mohammed Safy
Keyword(s):  
Medical Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Patient Privacy ◽  
Secret Code ◽  
The Mean ◽  
Image Quality Index

<p>Integrated healthcare systems require the transmission of medical images between medical centres. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the same time it preserves the image details. The performance of the proposed approach is evaluated using several performance measures such as the Mean Square Error (MSE), the Mean Absolute Error (MAE), the Peak Signal to Noise Ratio (PSNR), the Universal Image Quality Index (UIQI) and the Structural Similarity Index (SSIM). The proposed approach has been tested and shown robustness in detecting the intentional attacks that change image, specifically the most important diagnostic information.</p>

scholarly journals Research on the Image Denoising Method Based on Partial Differential Equations

2016 ◽  
Vol 16 (5) ◽  
pp. 109-118
Author(s):  
Xiaolu Xie
Keyword(s):  
Image Denoising ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
New Approach ◽  
Proposed Model ◽  
Index Measure

Abstract In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.

scholarly journals A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

2020 ◽  
Vol 12 (14) ◽  
pp. 2336 ◽  
Author(s):  
Shaobo Li ◽  
Jianhu Zhao ◽  
Hongmei Zhang ◽  
Zijun Bi ◽  
Siheng Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Low Rank ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Non Local ◽  
Better Than ◽  
Total Average

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

scholarly journals A COMPREHENSIVE REVIEW OF PANSHARPENING ALGORITHMS FOR GÖKTÜRK-2 SATELLITE IMAGES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 263-270 ◽  
Author(s):  
S. Kahraman ◽  
A. Ertürk
Keyword(s):  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Spectral Angle Mapper ◽  
Comprehensive Review ◽  
And Performance ◽  
Quality Assessments ◽  
Image Quality Index

In this paper, a comprehensive review and performance evaluation of pansharpening algorithms for GÖKTÜRK-2 images is presented. GÖKTÜRK-2 is the first high resolution remote sensing satellite of Turkey which was designed and built in Turkey, by The Ministry of Defence, TUBITAK-UZAY and Turkish Aerospace Industry (TUSAŞ) collectively. GÖKTÜRK-2 was launched at 18th. December 2012 in Jinguan, China and provides 2.5 meter panchromatic (PAN) and 5 meter multispectral (MS) spatial resolution satellite images. In this study, a large number of pansharpening algorithms are implemented and evaluated for performance on multiple GÖKTÜRK-2 satellite images. Quality assessments are conducted both qualitatively through visual results and quantitatively using Root Mean Square Error (RMSE), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), <i>Erreur Relative Globale Adimensionnelle de Synthése</i> (ERGAS), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI).

scholarly journals Evaluation of Severity of Infectious Pneumonia for Newborn Using Ultrasound Image under Adaptive Image Denoising Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jieqiong Liu ◽  
Tingting Lei ◽  
Fengyun Wu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Muscle Tension ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Structural Similarity ◽  
Critical Group ◽  
Ultrasound Images ◽  
Imaging Characteristics ◽  
Group A

This study was to analyze the ultrasound imaging characteristics of infectious pneumonia of newborn in different conditions and the differences in neurobehavioral development. An adaptive image denoising (AID) algorithm was constructed based on multiscale wavelet features. It was compared with the transform domain denoising (TDD) algorithm and spatial domain denoising (SDD) algorithm and applied to ultrasound images of newborns with infectious pneumonia. It was found that the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity index (FSIM) of the constructed algorithm were higher than those of the TDD and SDD algorithms ( P < 0.05 ). The ultrasound scores of newborns in noncritical group (group A, 1.54 ± 0.62 scores) were all lower than those of the critical group (group B, 3.96 ± 0.41 scores) and extremely critical group (group C, 4.25 ± 0.35 scores) ( P < 0.05 ). The behavioral ability, passive muscle tension, active muscle tension, and original reflection of the newborns in group A were better than other groups ( P < 0.05 ). It indicated that the constructed algorithm showed better denoising effect on ultrasound images, which could effectively evaluate the severity of newborns’ infectious pneumonia.

scholarly journals Weighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jiucheng Xu ◽  
Nan Wang ◽  
Zhanwei Xu ◽  
Keqiang Xu
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Lp Norm ◽  
Alternating Direction ◽  
Proposed Model ◽  
Feature Similarity Index ◽  
Constraint Problem ◽  
Error Constraint

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.

scholarly journals A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO

2021 ◽  
Vol 2070 (1) ◽  
pp. 012139
Author(s):  
V MNSSVKR Gupta ◽  
KVSS Murthy ◽  
R Shiva Shankar
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Threshold Function ◽  
Structural Similarity Index ◽  
Novel Approach ◽  
Accelerated Particle Swarm Optimization ◽  
And Performance ◽  
Social Group Optimization

Abstract Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).

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