scholarly journals Image Restoration Using Functional and Anatomical Information Fusion with Application to SPECT-MRI Images

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
S. Benameur ◽  
M. Mignotte ◽  
J. Meunier ◽  
J.-P. Soucy
Keyword(s):  
Image Restoration ◽  
Information Fusion ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Regularization Term ◽  
Restoration Technique ◽  
Ill Posed ◽  
Magnetic Resonance Imaging Mri ◽  
Restoration Method ◽  
Anatomical Information

Image restoration is usually viewed as an ill-posed problem in image processing, since there is no unique solution associated with it. The quality of restored image closely depends on the constraints imposed of the characteristics of the solution. In this paper, we propose an original extension of the NAS-RIF restoration technique by using information fusion as prior information with application in SPECT medical imaging. That extension allows the restoration process to be constrained by efficiently incorporating, within the NAS-RIF method, a regularization term which stabilizes the inverse solution. Our restoration method is constrained by anatomical information extracted from a high resolution anatomical procedure such as magnetic resonance imaging (MRI). This structural anatomy-based regularization term uses the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MRI and SPECT data volumes from each patient. This method was successfully tested on 30 pairs of brain MRI and SPECT acquisitions from different subjects and on Hoffman and Jaszczak SPECT phantoms. The experiments demonstrated that the method performs better, in terms of signal-to-noise ratio, than a classical supervised restoration approach using a Metz filter.

scholarly journals Restoring SAR images using Effective Image Restoration Approach

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Restoration ◽  
Error Rate ◽  
Satellite Images ◽  
Missing Values ◽  
Signal To Noise Ratio ◽  
Edge Matching ◽  
Landsat 7 ◽  
Data Source ◽  
Restoration Method ◽  
Boundary Reconstruction

Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.

scholarly journals Impact of 3 Tesla MRI on interobserver agreement in clinically isolated syndrome: A MAGNIMS multicentre study

2018 ◽  
Vol 25 (3) ◽  
pp. 352-360 ◽  
Author(s):  
Marloes HJ Hagens ◽  
Jessica Burggraaff ◽  
Iris D Kilsdonk ◽  
Serena Ruggieri ◽  
Sara Collorone ◽  
...  
Keyword(s):  
Interobserver Agreement ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Clinically Isolated Syndrome ◽  
Lesion Detection ◽  
Anatomical Region ◽  
Space And Time ◽  
3 Tesla Mri ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri

Background: Compared to 1.5 T, 3 T magnetic resonance imaging (MRI) increases signal-to-noise ratio leading to improved image quality. However, its clinical relevance in clinically isolated syndrome suggestive of multiple sclerosis remains uncertain. Objectives: The purpose of this study was to investigate how 3 T MRI affects the agreement between raters on lesion detection and diagnosis. Methods: We selected 30 patients and 10 healthy controls from our ongoing prospective multicentre cohort. All subjects received baseline 1.5 and 3 T brain and spinal cord MRI. Patients also received follow-up brain MRI at 3–6 months. Four experienced neuroradiologists and four less-experienced raters scored the number of lesions per anatomical region and determined dissemination in space and time (McDonald 2010). Results: In controls, the mean number of lesions per rater was 0.16 at 1.5 T and 0.38 at 3 T ( p = 0.005). For patients, this was 4.18 and 4.40, respectively ( p = 0.657). Inter-rater agreement on involvement per anatomical region and dissemination in space and time was moderate to good for both field strengths. 3 T slightly improved agreement between experienced raters, but slightly decreased agreement between less-experienced raters. Conclusion: Overall, the interobserver agreement was moderate to good. 3 T appears to improve the reading for experienced readers, underlining the benefit of additional training.

AN EFFICIENT RIPPLET-BASED SHRINKAGE TECHNIQUE FOR MR IMAGE RESTORATION

2014 ◽  
Vol 26 (06) ◽  
pp. 1450074
Author(s):  
A. Sumaiya Begum ◽  
S. Poornachandra
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
High Energy ◽  
Computational Time ◽  
Ripplet Transform ◽  
Anatomical Images ◽  
Magnetic Resonance Imaging Mri ◽  
Energy Compaction

In this paper a new ripplet-based shrinkage technique is used to suppress noise from Magnetic Resonance Imaging (MRI). The propitious properties of ripplet transform such as anisotropy, high directionality, good localization, and high-energy compaction make the proposed method efficient and feature preserving when compared to other transforms. Ripplet transform provides efficient representation of edges in images with a higher potential for image processing applications such as image restoration, compression, and de-noising. The proposed method implies a new nonlinear ripplet-based shrinkage technique to extract the spatial and frequency information from MRI corrupted by noise. The choice of this new shrinkage technique is due to its simplicity, versatility, and its efficiency in removing noise from homogenous regions and those regions with singularities, when compared to the existing filtering techniques. Experiments were conducted on several diffusion weighed images and anatomical images. The results show that the proposed de-noising technique shows competitive performance compared to the current state-of-art methods. Qualitative validation was performed based on several quality metrics and profound improvement over existing methods was obtained. Higher values of Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), mean structural similarity index (MSSIM), and lower values of Root Mean Square Error (RMSE) and computational time were obtained for the proposed ripplet-based shrinkage technique when compared to the existing ones.

Effect of SNR Estimation in Defocus Blurred Image Restoration

2012 ◽  
Vol 239-240 ◽  
pp. 1113-1117
Author(s):  
Feng Qing Qin
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Parameter Analysis ◽  
Snr Estimation ◽  
Blind Image Restoration ◽  
Blurred Image ◽  
Local Deviation ◽  
Restoration Method ◽  
Ringing Effect ◽  
Blind Image

A blind image restoration method is proposed to improve the quality of the image blurred by camera defocus and system noise. Firstly, the focus point spread function (PSF) of the blurred image is estimated through error-parameter analysis method. Secondly, the Signal-to-Noise Ratio (SNR) of the blurred image is estimated through local deviation method. Thirdly, utilizing the estimated defocus PSF and SNR, image restoration is performed through Wiener filtering method, in which circulation boundary method is adopted to reduce ringing effect. Experimental results show that the SNR of the blurred image is estimated approximately, and verify the great effect of SNR estimation in blind image restoration.

T2FRF Filter: An Effective Algorithm for the Restoration of Fingerprint Images

2021 ◽  
Author(s):  
Joycy K. Antony ◽  
K. Kanagalakshmi
Keyword(s):  
Image Restoration ◽  
Correlation Coefficient ◽  
Signal To Noise Ratio ◽  
Effective Algorithm ◽  
Main Challenge ◽  
Wide Range ◽  
Noisy Pixel ◽  
Degraded Image ◽  
Dim Light ◽  
Restoration Method

Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively.

Wiener Filter Based on the Image to Blur

2012 ◽  
Vol 591-593 ◽  
pp. 1567-1570
Author(s):  
Chao Da Chen ◽  
Si Qing Zhang ◽  
Chui Xin Chen
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Ill Posed ◽  
Fuzzy Image

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

scholarly journals A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 867 ◽  
Author(s):  
Jianhong Xiang ◽  
Pengfei Ye ◽  
Linyu Wang ◽  
Mingqi He
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
High Order ◽  
Computational Time ◽  
Total Variation Regularization ◽  
Selection Scheme ◽  
Staircase Effect ◽  
Restoration Method

This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

Sign in / Sign up

Export Citation Format

Share Document