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.

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.

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.

scholarly journals Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

2018 ◽  
Vol 10 (10) ◽  
pp. 1600 ◽  
Author(s):  
Chang Li ◽  
Yu Liu ◽  
Juan Cheng ◽  
Rencheng Song ◽  
Hu Peng ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Bilinear Model ◽  
Hyperspectral Unmixing ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

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.

scholarly journals Design a model of Image Restoration using AI in Digital Image Processing

2021 ◽  
Vol 12 (5) ◽  
pp. 862-865
Author(s):  
Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Motion Blur ◽  
Original Image ◽  
Source Imaging ◽  
Point Extension ◽  
Noise Image ◽  
Blurred Image

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

scholarly journals Review on Some Methods used in Image Restoration

2020 ◽  
pp. 13-16
Author(s):  
Rabab Farhan Abbas
Keyword(s):  
Image Restoration ◽  
Motion Blur ◽  
Measuring Device ◽  
Restoration Techniques

The restoration image is manner of mending the inventive image by eradicating noise and fuzziness from image. Image fuzziness is troublesome to shun in several things similar shooting, to confiscate motion blur caused by camera stillness, measuring device imaging to eradicate the outcome of image scheme retort, etc. The aim of image restoration is guesstimate the innovative image from surveillance image despoiled by haziness and preservative noise as much as promising. Altered image restoration techniques have urbanized by many researches. In this review I will discuss different images restoration methods.

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.

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

scholarly journals Platform motion blur image restoration system

2012 ◽  
Author(s):  
Stephen J. Olivas ◽  
Michal Šorel ◽  
Nima Nikzad ◽  
Joseph E. Ford
Keyword(s):  
Image Restoration ◽  
Motion Blur ◽  
Platform Motion
Sign in / Sign up

Export Citation Format

Share Document