scholarly journals MID Filter: An Orientation-Based Nonlinear Filter For Reducing Multiplicative Noise

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 936 ◽  
Author(s):  
Ibrahim Furkan Ince ◽  
Omer Faruk Ince ◽  
Faruk Bulut
Keyword(s):  
Multiplicative Noise ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Nonlinear Filter ◽  
Selection Function ◽  
Edge Preservation ◽  
Edge Preserving

In this study, an edge-preserving nonlinear filter is proposed to reduce multiplicative noise by using a filter structure based on mathematical morphology. This method is called the minimum index of dispersion (MID) filter. MID is an improved and extended version of MCV (minimum coefficient of variation) and MLV (mean least variance) filters. Different from these filters, this paper proposes an extra-layer for the value-and-criterion function in which orientation information is employed in addition to the intensity information. Furthermore, the selection function is re-modeled by performing low-pass filtering (mean filtering) to reduce multiplicative noise. MID outputs are benchmarked with the outputs of MCV and MLV filters in terms of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (MSE), standard deviation, and contrast value metrics. Additionally, F Score, which is a hybrid metric that is the combination of all five of those metrics, is presented in order to evaluate all the filters. Experimental results and extensive benchmarking studies show that the proposed method achieves promising results better than conventional MCV and MLV filters in terms of robustness in both edge preservation and noise removal. Noise filter methods normally cannot give better results in noise removal and edge-preserving at the same time. However, this study proves a great contribution that MID filter produces better results in both noise cleaning and edge preservation.

scholarly journals A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2346
Author(s):  
Tiago Wirtti ◽  
Evandro Salles
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Reconstruction Process ◽  
Regularization Term ◽  
Computational Performance

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.

scholarly journals Region of Interest Prediction using Segmentation

2020 ◽  
Vol 9 (5) ◽  
pp. 613-617
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Region Of Interest ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Edge Preservation ◽  
Segmentation Methods ◽  
Noise Ratio

Segmentation separates an image into different sections badsed on the desire of the user. Segmentation will be carried out in an image, until the region of interest (ROI) of an object is extracted. Segmentation reliability predicts the progress of the various segmentation techniques. In this paper, various segmentation methods are proposed and quality of segmentation is verified by using quality metrics like Mean Squared Error (MSE),Signal to Noise Ratio (SNR), Peak- Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Structural Similarity Index Metric (SSIM).

scholarly journals An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

2019 ◽  
Vol 2019 ◽  
pp. 1-25
Author(s):  
Yanzhu Hu ◽  
Jiao Wang ◽  
Xinbo Ai ◽  
Xu Zhuang
Keyword(s):  
Cost Function ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Segmentation Algorithm ◽  
Bee Colony ◽  
Segmentation Of Images ◽  
The Cost

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

scholarly journals A Characterization of CS-MRI Reconstruction Using PSO for Random Under Sampling Pattern

2019 ◽  
Vol 8 (2S11) ◽  
pp. 376-381
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Imaging Method ◽  
Radial Sampling ◽  
Under Sampling ◽  
Space Data ◽  
Sampling Pattern

MR imaging method is widely used for diagnosis applications. The echo signal received from the MR scanning machine is used to generate the image. The data acquisition and reconstruction are the important operations. In this paper the kspace is compressively sampled using Radial Sampling pattern for acquiring the k-space data and Particle Swarm Optimization (PSO) with Total Variation (TV) is used as the reconstruction algorithm for the faithful reconstruction of MR image. The experiments are conducted on MR images of Brain, Head Angiogram and Shoulder images. Performance of the proposed method of reconstruction is analyzed for different sampling kspace scanning percentages. The reconstruction results are compared with the standard sampling pattern used for compressive sampling prove the novelty of the proposed method. The results are verified in terms of Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and Structural Similarity index (SSIM).

Gradient nuclear norm minimization-based image filter

2019 ◽  
Vol 33 (19) ◽  
pp. 1950214 ◽  
Author(s):  
Saurabh Khare ◽  
Praveen Kaushik
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Nuclear Norm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Edge Preservation ◽  
Nuclear Norm Minimization ◽  
Norm Minimization ◽  
Filtering Technique

Designing an efficient filtering technique is an ill-posed problem especially for image affected from high density of noise. The majority of existing techniques suffer from edge degradation and texture distortion issues. Therefore, in this paper, an efficient weighted nuclear norm minimization (NNM)-based filtering technique to preserve the edges and texture information of filtered images is proposed. The proposed technique significantly improves the quantitative improvements on the low rank approximation of nonlocal self-similarity matrices to deal with the overshrink problem. Extensive experiments reveal that the proposed technique preserves edges and texture details of filtered image with lesser number of visual artifacts on visual quality. The proposed technique outperforms the existing techniques over the competitive filtering techniques in terms of structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR) and edge preservation index (EPI).

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).

Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

2020 ◽  
Vol 20 (04) ◽  
pp. 2050032
Author(s):  
Rubul Kumar Bania ◽  
Anindya Halder
Keyword(s):  
Breast Cancer ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Statistical Significance ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Quantitative Indices ◽  
Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.

Video Steganography Based on Shi-Tomasi Corner Detection and Least Significant Bit Algorithm

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Kaviya K ◽  
Mridula Bala ◽  
Swathy N P ◽  
Chittam Jeevana Jyothi ◽  
S.Ewins Pon Pushpa
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Corner Detection ◽  
Least Significant Bit ◽  
Secret Data ◽  
Video Steganography ◽  
Corner Points ◽  
Algorithmic Rule

Today, the digital and social media platforms are extremely trending, leading a demand to transmit knowledge very firmly. The information that is exchanged daily becomes ‘a victim’ to hackers. To beat this downside, one of the effective solutions is Steganography or Cryptography. In this paper, the video Steganography and cryptography thoughts are employed, where a key text is hidden behind a ‘certain frame’ of the video using Shi-Tomasi corner point detection and Least Significant Bit (LSB) algorithmic rule. Shi-Tomasi algorithmic rule is employed to observe, the corner points of the frame. In the proposed work, a ‘certain frame’ with large number of corner points is chosen from the video. Then, the secret text is embedded within the detected corner points using LSB algorithmic rule and transmitted. At the receiver end, decryption process is employed, in the reverser order of encryption to retrieve the secret data. As a technical contribution, the average variation of Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index are analysed for original and embedded frames and found to be 0.002, 0.016 and 0.0018 respectively.

scholarly journals Gated Dehazing Network via Least Square Adversarial Learning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6311
Author(s):  
Eunjae Ha ◽  
Joongchol Shin ◽  
Joonki Paik
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Least Square ◽  
Adversarial Learning ◽  
Scattering Model ◽  
Atmospheric Scattering ◽  
Proposed Model ◽  
Atmospheric Scattering Model

In a hazy environment, visibility is reduced and objects are difficult to identify. For this reason, many dehazing techniques have been proposed to remove the haze. Especially, in the case of the atmospheric scattering model estimation-based method, there is a problem of distortion when inaccurate models are estimated. We present a novel residual-based dehazing network model to overcome the performance limitation in an atmospheric scattering model-based method. More specifically, the proposed model adopted the gate fusion network that generates the dehazed results using a residual operator. To further reduce the divergence between the clean and dehazed images, the proposed discriminator distinguishes dehazed results and clean images, and then reduces the statistical difference via adversarial learning. To verify each element of the proposed model, we hierarchically performed the haze removal process in an ablation study. Experimental results show that the proposed method outperformed state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), international commission on illumination cie delta e 2000 (CIEDE2000), and mean squared error (MSE). It also gives subjectively high-quality images without color distortion or undesired artifacts for both synthetic and real-world hazy images.

Comparative study of statistical modeling of wavelet coefficients for SAR image despeckling

2017 ◽  
Vol 15 (01) ◽  
pp. 1750003 ◽  
Author(s):  
M. N. Sumaiya ◽  
R. Shantha Selva Kumari
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Computing Time ◽  
Similarity Index ◽  
Structural Similarity ◽  
Quality Metrics ◽  
Posteriori Probability ◽  
Edge Preservation ◽  
Gaussian Probability Density Function ◽  
A Posteriori Probability

The work is concentrated to get good despeckled performance under non-homomorphic framework by involving simple distributions to obtain closed form Maximum-a-Posteriori probability (MAP) solutions. To estimate noise-free wavelet coefficient, distributions with lesser number of parameters allied Laplacian/Gaussian probability density function (pdf) for signal reflectivity and Rayleigh/Gaussian pdf for noisy signal are employed. Thus, the four despeckling methods are formed, namely LRMAP, GRMAP, LGMAP and GGMAP to despeckle SAR images and the effectiveness of different distributions is studied. The despeckling method is made adaptive by estimating the local variance of high frequency image using wavelet sub-band coefficient statistics. Also, the parameter space used in the proposed methods does not involve initialization, iterative search and convergence problems. The performances of the proposed methods are evaluated in terms of Equivalent Number of Looks (ENL), Peak Signal to Noise Ratio (PSNR), Edge Preservation ([Formula: see text] and Mean Structural Similarity Index Measure (MSSIM). Experimental results show that LRMAP yields good results over all methods and GGMAP does not perform good for all images in terms of all quality metrics. Also, the proposed methods yield good quality metrics in less computing time as compared with the method available in the literature.

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