scholarly journals Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

2020 ◽  
Vol 30 (1) ◽  
pp. 240-257
Author(s):  
Akula Suneetha ◽  
E. Srinivasa Reddy
Keyword(s):  
Image Quality ◽  
Mean Square Error ◽  
Fuzzy Set ◽  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Structural Similarity ◽  
Fuzzy Rules ◽  
Mean Square ◽  
Image Edges

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

scholarly journals A DENOISING OF BIOMEDICAL IMAGES

2015 ◽  
Vol XL-5/W6 ◽  
pp. 73-78 ◽  
Author(s):  
D. N. H. Thanh ◽  
S. D. Dvoenko
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Poisson Noise ◽  
Biomedical Images ◽  
Biomedical Image ◽  
Digital Devices ◽  
Imaging Science

Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).

scholarly journals Measure of Singular Value Decomposition (M-SVD) based Quality Assessment for Medical Images with Degradation

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Ersin Elbasi
Keyword(s):  
Image Quality ◽  
Singular Value Decomposition ◽  
Mean Square Error ◽  
Similarity Index ◽  
Quality Measurement ◽  
Structural Similarity ◽  
Singular Value ◽  
Mean Square ◽  
Structural Similarity Index ◽  
Value Decomposition

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

scholarly journals Image Restoration by Linear Regression for Gaussian Noise Removal from Natural Images

2019 ◽  
Vol 8 (11S2) ◽  
pp. 126-130
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Gaussian Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Support Vector ◽  
Mean Square ◽  
Degraded Image ◽  
Index Measure

Image restoration improves the features information of degraded or corrupted image. The degradation of image because of addition of noise when acquiring the image. Many algorithms are developed by many researches. In this paper image is corrupted by Gaussian noise to generate degraded image. The image is restored from this degraded image by supervised learning based algorithm. Few images are considered for training the dictionary with each element of size 9x9. The degraded image is considered patch by patch for restoring the patch from the trained set of images by support vector machine. The quality assessment of the image done by comparing the quality matrices like mean square error, root mean square error, peak signal to noise ratio, structural similarity index measure and feature similarity index measure. In this paper the images are considered are cameraman, house, Lena, Barbara and Parrot

Master Frame Extraction of Fetal Cardiac Images Using B Mode Ultrasound Images

2022 ◽  
Vol 54 ◽  
pp. 51-64
Author(s):  
Sushma Tumkur Venugopal ◽  
Sriraam Natarajan ◽  
Megha P. Arakeri ◽  
Suresh Seshadri
Keyword(s):  
Mean Square Error ◽  
Hausdorff Distance ◽  
Cardiac Cycle ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Similarity Index ◽  
Structural Similarity ◽  
Ultrasound Images ◽  
Mean Square ◽  
Structural Similarity Index

Fetal Echocardiography is used for monitoring the fetal heart and for detection of Congenital Heart Disease (CHD). It is well known that fetal cardiac four chamber view has been widely used for preliminary examination for the detection of CHD. The end diastole frame is generally used for the analysis of the fetal cardiac chambers which is manually picked by the clinician during examination/screening. This method is subjected to intra and inter observer errors and also time consuming. The proposed study aims to automate this process by determining the frame, referred to as the Master frame from the cine loop sequences that can be used for the analysis of the fetal heart chambers instead of the clinically chosen diastole frame. The proposed framework determines the correlation between the reference (first) frame with the successive frames to identify one cardiac cycle. Then the Master frame is formed by superimposing all the frames belonging to one cardiac cycle. The master frame is then compared with the clinically chosen diastole frame in terms of fidelity metrics such as Dice coefficient, Hausdorff distance, mean square error and structural similarity index. The average value of the fidelity metrics considering the dataset used for this study 0.73 for Dice, 13.94 for Hausdorff distance, 0.99 for Structural Similarity Index and 0.035 for mean square error confirms the suitability of the proposed master frame extraction thereby avoiding manual intervention by the clinician. .

Digital Imaging Exposure-Time Optimisation of Tripod-Camera Vibrating Systems Based on MTF

2008 ◽  
Author(s):  
Giulio Fanti ◽  
Roberto Basso
Keyword(s):  
Image Quality ◽  
Exposure Time ◽  
Digital Image ◽  
Digital Imaging ◽  
Digital Images ◽  
Noise Sources ◽  
Initial Analysis ◽  
Vibrating System ◽  
Time Optimization ◽  
Vibrating Systems

The problem of exposure-time optimization in digital images acquired by a tripod-camera vibrating system is examined in this paper and an initial analysis is presented. The different noise sources concerning both the acquisition sensor in the camera and external vibrations were studied and quantified in some specific cases. The digital image quality is then discussed in terms of the MTF function evaluated at 50% level in order to define what the optimum ranges of exposure-times are.

Evaluation of digital and film hemispherical photography and spherical densiometry for measuring forest light environments

2000 ◽  
Vol 30 (12) ◽  
pp. 1999-2005 ◽  
Author(s):  
Sylvia R Englund ◽  
Joseph J O'Brien ◽  
David B Clark
Keyword(s):  
Image Quality ◽  
Computer Analysis ◽  
Digital Image ◽  
Digital Images ◽  
Optical Quality ◽  
Field Of View ◽  
Canopy Openness ◽  
Hemispherical Photography ◽  
Site Factor ◽  
Film Images

This study presents the results of a comparison of digital and film hemispherical photography as means of characterizing forest light environments and canopy openness. We also compared hemispherical photography to spherical densiometry. Our results showed that differences in digital image quality due to the loss of resolution that occurred when images were processed for computer analysis did not affect estimates of unweighted openness. Weighted openness and total site factor estimates were significantly higher in digital images compared with film photos. The differences between the two techniques might be a result of underexposure of the film images or differences in lens optical quality and field of view. We found densiometer measurements significantly increased in consistency with user practice and were correlated with total site factor and weighted-openness estimates derived from hemispherical photography. Digital photography was effective and more convenient and inexpensive than film cameras, but until the differences we observed are better explained, we recommend caution when comparisons are made between the two techniques. We also concluded that spherical densiometers effectively characterize forest light environments.

scholarly journals Estimation in Gaussian Noise: Properties of the Minimum Mean-Square Error

2011 ◽  
Vol 57 (4) ◽  
pp. 2371-2385 ◽  
Author(s):  
Dongning Guo ◽  
Yihong Wu ◽  
Shlomo Shamai ◽  
Sergio Verdú
Keyword(s):  
Mean Square Error ◽  
Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Mean Square

The Cramer–Rao lower bound in a non-AWGN model for the affine phase retrieval

2021 ◽  
pp. 2050089
Author(s):  
Zhitao Zhuang ◽  
Kaixin Wang
Keyword(s):  
Lower Bound ◽  
Mean Square Error ◽  
Gaussian Noise ◽  
Phase Retrieval ◽  
Additive White Gaussian Noise ◽  
White Gaussian Noise ◽  
Mean Square ◽  
Cramer Rao Lower Bound ◽  
The Difference

In this paper, we derive the Cramer–Rao lower bound (CRLB) in a non-additive white Gaussian noise (AWGN) model for the affine phase retrieval (APR) and simulate the difference of CRLB and mean square error produced by PhaseLift of phase retrieval and APR in AWGN and non-AWGN cases.

scholarly journals The Level Weighted Structural Similarity Loss: A Step Away from MSE

2019 ◽  
Vol 33 ◽  
pp. 9989-9990 ◽  
Author(s):  
Yingjing Lu
Keyword(s):  
Mean Square Error ◽  
Structural Similarity ◽  
Generative Models ◽  
Spatial Relationships ◽  
Mean Square ◽  
Model Reconstruction ◽  
Image Domain ◽  
Similarity Metric ◽  
The Mean

The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on various Auto-Encoder variants show that our loss is able to outperform the MSE loss and the Vanilla SSIM loss. We also provide reasons why our model is able to succeed in cases where the standard SSIM loss fails.

Sign in / Sign up

Export Citation Format

Share Document