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.

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

Optimum Color and Contrast Enhancement for Online Ferrography Image Restoration

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Lingfeng Yang ◽  
Tonghai Wu ◽  
Kunpeng Wang ◽  
Hongkun Wu ◽  
Ngaiming Kwok
Keyword(s):  
Image Restoration ◽  
Contrast Enhancement ◽  
Real Time ◽  
Lagrange Multiplier ◽  
Information Loss ◽  
Cost Functions ◽  
High Quality ◽  
Degraded Image ◽  
Restoration Method ◽  
The Cost

Online ferrography, because of its nondestructive and real-time capability, has been increasingly applied in monitoring machine wear states. However, online ferrography images are usually degraded as a result of undesirable image acquisition conditions, which eventually lead to inaccurate identifications. A restoration method focusing on color correction and contrast enhancement is developed to provide high-quality images for subsequent processing. Based on the formation of a degraded image, a model describing the degradation is constructed. Then, cost functions consisting of colorfulness, contrast, and information loss are formulated. An optimal restored image is obtained by minimizing the cost functions, in which parameters are properly determined using the Lagrange multiplier. Experiments are carried out on a collection of online ferrography images, and results show that the proposed method can effectively improve the image both qualitatively and quantitatively.

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 Interactive Entertainment Image Restoration Method Based on Hopfield Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01008
Author(s):  
Shuangqing lv
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Original Data ◽  
Small Scale ◽  
Interactive Entertainment ◽  
Degraded Image ◽  
Restoration Method ◽  
Traditional Image ◽  
Better Than

The traditional image restoration methods of interactive entertainment are based on the original data. This paper proposes an interactive entertainment image restoration method based on Hopfield neural network. Firstly, the nonlinear mapping relationship between the degraded image and the real image is preliminarily established through the network, and then optimized by the algorithm. Finally, the image restoration can be achieved through the network. The experiments show that it has higher feasibility and the recovery effect on small-scale blur is better than the existing method.

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.

Multiframe Turbulence-Degraded Image Restoration Method Based on Temporal Signature

2013 ◽  
Vol 50 (12) ◽  
pp. 120101
Author(s):  
邵慧 Shao Hui ◽  
汪建业 Wang Jianye ◽  
徐鹏 Xu Peng ◽  
杨明翰 Yang Minghan
Keyword(s):  
Image Restoration ◽  
Degraded Image ◽  
Restoration Method

scholarly journals Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

2021 ◽  
Vol 36 (1) ◽  
pp. 642-649
Author(s):  
G. Sharvani Reddy ◽  
R. Nanmaran ◽  
Gokul Paramasivam
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Degraded Image ◽  
Restoration Algorithms ◽  
Index Measure

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

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.

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 Performance Evaluation and Comparison of Different Noise, Apply on PNG Image Format Used in Deconvolution Wiener Filter (FFT) Algorithm

2015 ◽  
Vol 4 ◽  
pp. 8-14 ◽  
Author(s):  
Kalpana Chaurasia ◽  
Nidhi Sharma
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Absolute Error ◽  
Mean Square ◽  
Degraded Image ◽  
Blurred Image ◽  
Degraded Images

Image Restoration is a field of Image Processing. This deals with recovering an original and sharp image from a degraded image using degradation & restoration function. This study focus on restoration of degraded images which have been blurred by known degradation function. PNG (Tag Index Format) are considered for analyzing the image restoration techniques deconvolution using wiener filter (FFT) algorithm with an information of the Point Spread Function (PSF) corrupted blurred image and then corrupted by Different noise. Performance analysis is done to measure the efficiency by which image is recovered. The analysis is done on the basis of various performance metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error(MSE),Root Mean Square Error (RMSE), Mean Absolute Error (MAE).

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