scholarly journals Efficient Image Restoration Methods for Image Recovery

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3507-3511
Keyword(s):  
Image Restoration ◽  
Frequency Domain ◽  
Spatial Domain ◽  
Original Form ◽  
Original Image ◽  
Frequency Domain Methods ◽  
Restoration Techniques ◽  
Degraded Image ◽  
The One ◽  
Processing Techniques

Image Restoration is the one of the significant techniques to process an image. The process of taking a noisy image and obtaining the clean, original image is called as image restoration. This process is applied in every field where images have to be understood and analyzed. Image restoration is a method of recovering an original image from a degraded image. To restore corrupted image into its original form restoration techniques are used. The restoration techniques mainly focused to improve the image quality. Usually image processing techniques are implemented in the two domains, they are frequency domain or spatial domain. This paper mainly focused on different approaches to restoration, variations between frequency domain methods and spatial domain methods. Especially the relation between spatial and frequency resolutions and various filters in spatial and frequency domain. The present work shows the performance of different kinds of filters and these filters are analyzed by implementing and simulating on MATLAB.

Image Restoration using Recent Techniques: A Survey

2021 ◽  
pp. 13050-13062
Author(s):  
Mrs. Poonam Y. Pawar, Dr. Bharati Sanjay Ainapure
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Digital Image ◽  
Medical Science ◽  
Computer Algorithms ◽  
Original Image ◽  
Performance Improvements ◽  
Restoration Techniques ◽  
Degraded Image ◽  
Recent Trends

Image Restoration is one of the challenging and essential milestones in the image processing domain. Digital image processing is a technique for manipulating digital images using a variety of computer algorithms. The process of transforming the degraded or damaged image to the original image can be known as Image Restoration. The image restoration process improves image quality by converting the degraded image into the original clean image. The techniques for image restoration are comprised of predefined parameters through which digital image gets processed for refinements. The purpose of restoration is to start with the acquired image and then estimate the original image as accurately as possible. A degraded image can be contaminated by any of a blur or noise or both. Many factors can contribute to image degradation, including poor capture, poor lighting, and poor eyesight. Medical science, defensive sensor systems, forensic detections, and astrology all rely on image restoration for accuracy. This paper discusses various image restoration techniques using recent trends for performance improvements.

A PUBLIC MESH WATERMARKING ALGORITHM BASED ON ADDITION PROPERTY OF FOURIER TRANSFORM

2006 ◽  
Vol 06 (01) ◽  
pp. 35-43 ◽  
Author(s):  
LI LI ◽  
ZHIGENG PAN ◽  
DAVID ZHANG
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Spatial Domain ◽  
Experimental Results ◽  
Original Image ◽  
The Fourier Transform

This paper presents a public mesh watermarking algorithm whereby the resultant watermarked image minus the original image is the watermark information. According to the addition property of the Fourier transform, a change of spatial domain will cause a change in the frequency domain. The watermark information is then scaled down and embedded in one part of the x-coordinate of the original mesh. Finally, the x-coordinate of the test mesh is amplified before extraction. Experimental results prove that our algorithm is resistant to a variety of attacks without the need for any preprocessing.

scholarly journals Comparative Study of Major Image Enhancement Algorithms

2017 ◽  
Vol 2 (7) ◽  
pp. 23
Author(s):  
Amrutha Kulkarni ◽  
Shanta Rangaswamy ◽  
Manonmani S
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Image Enhancement ◽  
Original Image ◽  
Inverse Process ◽  
Restoration Techniques

Image restoration is a process of reconstruction or recovery of an image that has been corrupted or degraded by any degradation phenomenon. Image restoration techniques are inclined towards modeling the degradation and applying the inverse process in order to recover the original image. The critical goal of restoration techniques is to improve the quality of an image in some predefined manner. This present paper is a comparative study of image enhancement techniques used for improving the quality of a given image and evaluate it against the quality of a given image and evaluate it against SNR, PSNR, MSE, and SSIM as metrics.

Advance in Image and Audio Restoration and their Assessments: A Review

2021 ◽  
Vol 12 (2) ◽  
pp. 1-16
Author(s):  
Omar H. Mohammed ◽  
Basil Sh. Mahmood
Keyword(s):  
Image Restoration ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Degradation Process ◽  
Motion Blur ◽  
Original Image ◽  
Restoration Techniques ◽  
Degradation Models ◽  
Noise Impulse ◽  
Audio Restoration

Image restoration is the process of restoring the original image from a degraded one. Images can be affected by various types of noise, such as Gaussian noise, impulse noise, and affected by blurring, which is happened during image recordings like motion blur, Out-of-Focus Blur, and others. Image restoration techniques are used to reverse the effect of noise and blurring. Restoration of distorted images can be done using some information about noise and the blurring nature or without any knowledge about the image degradation process. Researchers have proposed many algorithms in this regard; in this paper, different noise and degradation models and restoration methods will be discussed and review some researches in this field.

scholarly journals A Novel Real-Time Image Restoration Algorithm in Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xingmin Ma ◽  
Shenggang Xu ◽  
Fengping An ◽  
Fuhong Lin
Keyword(s):  
Image Restoration ◽  
Real Time ◽  
Edge Computing ◽  
Support Vector ◽  
Original Image ◽  
Edge Node ◽  
Restoration Algorithm ◽  
Real Time Image ◽  
Degraded Image ◽  
Time Image

Owning to the high processing complexity, the image restoration can only be processed offline and hardly be applied in the real-time production life. The development of edge computing provides a new solution for real-time image restoration. It can upload the original image to the edge node to process in real time and then return results to users immediately. However, the processing capacity of the edge node is still limited which requires a lightweight image restoration algorithm. A novel real-time image restoration algorithm is proposed in edge computing. Firstly, 10 classical functions are used to determine the population size and maximum iteration times of traction fruit fly optimization algorithm (TFOA). Secondly, TFOA is used to optimize the optimal parameters of least squares support vector regression (LSSVR) kernel function, and the error function of image restoration is taken as an adaptive function of TFOA. Thirdly, the LLSVR algorithm is used to restore the image. During the image restoration process, the training process is to establish a mapping relationship between the degraded image and the adjacent pixels of the original image. The relationship is established; the degraded image can be restored by using the mapping relationship. Through the comparison and analysis of experiments, the proposed method can meet the requirements of real-time image restoration, and the proposed algorithm can speed up the image restoration and improve the image quality.

scholarly journals Time-Domain and Monostatic-Like Frequency-Domain Methods for Bistatic SAR Simulation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5012
Author(s):  
Gerardo Di Martino ◽  
Antonio Iodice ◽  
Antonio Natale ◽  
Daniele Riccio
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Transfer Functions ◽  
Practical Interest ◽  
Simulation Tools ◽  
Bistatic Sar ◽  
Frequency Domain Methods ◽  
Spread Function ◽  
Physical Information ◽  
The One

In recent years, an increasing interest has been devoted to bistatic SAR configurations, which can be effectively used to improve system performance and/or to increase the amount of physical information retrievable from the observed scene. Within this context, the availability of simulation tools is of paramount importance, for both mission planning and processing algorithm verification and testing. In this paper, a time domain simulator useful to obtain the point-spread function and the raw signal for the generic bistatic SAR configuration is presented. Moreover, we focus on the case of two bistatic configurations, which are of considerable interest in actual SAR applications, i.e., the translational invariant SAR and the one-stationary SAR acquisition geometries, for which we obtain meaningful expressions of the Transfer Functions. In particular, these expressions are formally equal to those obtained for the monostatic SAR configuration, so that the already available monostatic simulator can be easily adapted to these bistatic cases. The point-target raw signals obtained using the (exact) time domain simulator and the (approximated) frequency domain one are compared, with special attention to acquisition geometries that may be of practical interest in Formation-Flying SAR applications. Results show that the phase difference between raw signals simulated with the two approaches is, in all cases, smaller (and often much smaller) than about 10 degrees, except that at the very edge of the raw signals, where however, it does not exceed about 50 degrees.

scholarly journals Noise Estimation using Kalman Filter in Image Restoration

2019 ◽  
Vol 8 (6S2) ◽  
pp. 888-891
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Image Restoration ◽  
Medical Image ◽  
Blind Deconvolution ◽  
Medical Images ◽  
Medical Image Processing ◽  
Noise Removal ◽  
Original Image ◽  
Degraded Image

Medical image processing plays a vital role in medical sciences from the past decades. Medical image processing becomes simple and useful with the advancement of image processing techniques. Medical images are used to observe the information related to inside the organs of human body. For better diagnoses and analysis of disease the image should be clear, noise free and more informative also. Usually medical images are corrupted by different noises in image acquisition and transmission process. The basic challenge in medical image processing is noise removal without losing diagnostic information. Image restoration is the one of the technique to recover the original image from the degraded image. In this paper, we are proposing a kalman filter to estimate the noise function from the degraded image and to reconstruct the original image. Here we are taking into account that the medical image was corrupted by the gaussian, speckle and salt & pepper noise. The simulation result infers that the proposed blind deconvolution method can be able to suppress the noise well and also preserve edge information without losing diagnostic data.

ANALISIS RESTORASI CITRA KABUR ALGORITMA WIENER MENGGUNAKAN INDEKS KUALITAS CITRA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Nur Afiyat
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Quality Index ◽  
Input Image ◽  
Motion Blur ◽  
Image Motion ◽  
Original Image ◽  
Degraded Image ◽  
Gaussian Blur ◽  
Image Quality Index

Degradation and additional noise in an image will make the quality decreases. Image restoration is needed to restore the image quality to be similar to the original state. Damage to the image can restored include: blurred image, the image with noise spots, dual image, over-saturated color, and the pixel error. To make theblur image is modeled as a convolution between the original image with the point spread function (PSF) which is a point or object spectrum will be spread out so that objects appear to fade. Image restoration is done by passing a blurry image on a filter. In this study discussed Wiener image restoration algorithm using the input image is degraded motion blur and Gaussian blur. Quality image restoration results were analyzed using the image quality index, by comparing the image of the restoration of the original image as a reference. Further image restoration results used as the input image is then processed using Index Image Analysis GUI application. Each of the input image must have a resolution and dimensions that are identical to a reference image. The results showed that by providing opaqueness different models on the same image, the degree of blurring that occurs will be different. Image quality index results for the restoration of degraded image higher than the Gaussian blur image of the restoration of degraded image motion blur. Image quality index results for the restoration of degraded image motion blur ranged from 0.84229 up to 0.87146. Image quality index results for the restoration of degraded Gaussian blur images ranging from 0.86969 up to 0.90025.Keywords: Restoration, blur image, PSF, Wiener algorithm, the image quality index.

A Kind of DWT Domain Image Digital Watermarking Algorithm Research

2014 ◽  
Vol 1044-1045 ◽  
pp. 991-994
Author(s):  
Tao Liu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Digital Watermarking ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Original Image ◽  
Adaptive Watermarking ◽  
Frequency Domain Methods

An image-adaptive watermarking algorithm based on wavelet transform was proposed. At first, A digital image used as watermarking was scrambled. Next, the original image was decomposed by discrete wavelet transform,and in accordance with the characteristics of human visual system, wavelet decomposition in the low-frequency domain, Methods which average of adjacent domain instead of single wavelet decomposition coefficients was used to estimate and quantitative, watermarking was adaptively embedded in wavelet coefficients of low-frequency domain. At last, the simulation experimental results show that the algorithm for a variety of conventional image processing has good robustness.

scholarly journals Image Deconvolution by Means of Frequency Blur Invariant Concept

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Barmak Honarvar Shakibaei ◽  
Peyman Jahanshahi
Keyword(s):  
Point Spread Function ◽  
Fourier Transforms ◽  
Spatial Domain ◽  
Image Deconvolution ◽  
Original Image ◽  
Invariant Features ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
The Moment

Different blur invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the moment domain. In this paper, a frequency framework is proposed to develop blur invariant features that are used to deconvolve a degraded image caused by a Gaussian blur. These descriptors are obtained by establishing an equivalent relationship between the normalized Fourier transforms of the blurred and original images, both normalized by their respective fixed frequencies set to one. Advantage of using the proposed invariant descriptors is that it is possible to estimate both the point spread function (PSF) and the original image. The performance of frequency invariants will be demonstrated through experiments. An image deconvolution is done as an additional application to verify the proposed blur invariant features.

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