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 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.

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 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 Maize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)

2018 ◽  
Vol 7 (3.20) ◽  
pp. 402
Author(s):  
Prihastuti Harsan ◽  
Arie Qurania ◽  
Karina Damayanti
Keyword(s):  
Image Processing ◽  
Zea Mays ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Disease ◽  
Zea Mays L ◽  
Maize Plant ◽  
Computer Algorithms ◽  
K Nearest Neighbor ◽  
Plant Pests

Plant pests of maize are known to attack in all phases of corn plant growth (Zea mays L. saccharata), both vegetative and generative. Common pests found in maize are seed flies (Atherigona sp.), Stem borers (Ostrinia furnacalis), Boricoverpa armigera, leaf-eaters (Spodoptera litura). The process of identification of maize plant disease is done through laboratory analysis and direct observation. The time required to obtain the identification result is 4 (four) months. Plant pests will attack some parts of the plant, including leaves, stems and fruit. Early detection is usually done through leaves. Plant pests will attack the plant leaf area with certain characteristics. Digital image processing is the use of computer algorithms to perform image processing on digital images. Identification of maize plant disease can apply image processing techniques through the characteristics or symptoms of disease raised on the leaves. Characteristic of attacks by pests in maize plants can be detected through the colors and patterns that appear on the leaves. This research performs implementation of digital image processing method to identify disease in maize plant caused by pest. The disease is Hawar Leaf, Bulai (Downy Midew), Hama Grasshopper, Leaf Spot (Sourthern Leaf Blight). Through color and edge detection, the accuracy obtained is 91.7%. 

scholarly journals Exploring Image Processing and Image Restoration Techniques

2015 ◽  
Vol 15 (3) ◽  
pp. 172-179
Author(s):  
Batyrkhan Sultanovich Omarov ◽  
Aigerim Bakatkaliyevna Altayeva ◽  
Young Im Cho
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Restoration Techniques

A Research for Image Defogging Algorithm

2013 ◽  
Vol 409-410 ◽  
pp. 1653-1656 ◽  
Author(s):  
Yu Fan ◽  
Xue Feng Wu
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Physical Model ◽  
Digital Image Processing ◽  
Digital Image ◽  
Computational Photography ◽  
Processing Technology ◽  
Image Process ◽  
Image Defogging ◽  
Resolution Algorithm

Computational photography and image processing technology are used to restore the clearness of images taken in fog scenes autmatically.The technology is used to restore the clearness of the fog scene,which includes digital image processing and the physical model of atmospheric scattering.An algorithm is designed to restore the clearness of the fog scene under the assumption of the albedo images and then the resolution algorithm is analysised.The algorithm is implemented by the software of image process ,which can improve the efficiency of the algorithm and interface.The fog image and defogging image are compared, and the results show that the visibility of the image is improved, and the image restoration is more clearly .

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 Application of Different Filtering Techniques in Digital Image Processing

2021 ◽  
Vol 2062 (1) ◽  
pp. 012007
Author(s):  
Sumant Sekhar Mohanty ◽  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Noise ◽  
Noise Model ◽  
Electronic Noise ◽  
Original Image ◽  
Color Information ◽  
Processing Steps ◽  
Filtering Techniques

Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.

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.

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