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 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.

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.

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.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Combined exposure computational imaging system and image restoration method

2021 ◽  
Vol 29 (2) ◽  
pp. 452-462
Author(s):  
Xiao-tian WU ◽  
◽  
Bo LÜ ◽  
Bo LIU ◽  
Hang YANG ◽  
...  
Keyword(s):  
Image Restoration ◽  
Imaging System ◽  
Computational Imaging ◽  
Combined Exposure ◽  
Restoration Method

scholarly journals IMAGE RESTORATION METHOD, INVARIANT TO SIGNAL CORRELATION IN THE INFORMATION SYSTEM WITH ADAPTIVE ANTENNA ARRAY

2020 ◽  
pp. 64-76
Author(s):  
V.V. Skachkov ◽  
Keyword(s):  
Information System ◽  
Image Restoration ◽  
Antenna Array ◽  
Correlation Matrix ◽  
Signal To Noise Ratio ◽  
Weight Vector ◽  
Adaptive Antenna ◽  
Adaptive Antenna Array ◽  
Optimal Weight ◽  
Optimal Weight Vector

The problem of image signal processing in the information system with adaptive antenna array based on the inversion of sample estimates of correlation matrix of observations is considered. The example of the maximum signal-to-noise ratio criterion shows the problem, inherent in classical methods of finding the optimal weight vector under a priori uncertainty conditions when detecting correlated image signals. It has been concluded that the dependence of these methods on the inverse of estimation of the correlation matrix of observations leads to the impossibility of separating correlated image signals. As a consequence, the use of classical methods of finding the optimal weight vector in the information system with adaptive antenna array is effective only in the case of image restoration from a single signal source, with the signal received on the set of independent jamming background. A novel method, invariant to the correlation of image signals, has been developed for finding the optimal weight vector without the usage of correlation matrix of observations. An image restoration algorithm invariant to correlation of image signals in the information system with adaptive antenna array is proposed. Statistical models have been constructed for the classical method based on the criterion of maximum signal-to-noise ratio and invariant to correlation method of image restoration in following cases: a single source against the jamming background of two independent sources; two independent sources against the jamming background. Simulation results in the information system with adaptive antenna array are presented, showing to visually assess efficiency of proposed methods of image signal restoration using optimal weight vector. Detailed analysis of the results obtained is carried out.

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