Статті в журналах: "Digital image restoration"

1

Banham, M. R., and A. K. Katsaggelos. "Digital image restoration." IEEE Signal Processing Magazine 14, no. 2 (March 1997): 24–41. http://dx.doi.org/10.1109/79.581363.

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2

Kirkland, Earl J. "Digital restoration of ADF-STEM images." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 832–33. http://dx.doi.org/10.1017/s0424820100106223.

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Frieden and Wells have derived a maximum likelihood (ML) image restoration algorithm that accurately models the noise in each image element using Poisson counting statistics. This method also leads naturally to “maximum entropy” like ideas. They have reported a rather dramatic resolution enhancement when this method was applied to low light level astronomical images in which the image noise was essentially due to photon counting statistics. This low light level situation also accurately models the ADF (annular dark field) STEM (scanning transmission microscope) image if the image is aquired digitally by electronically counting the individual scattered electrons for each position of the focused probe.The simplest ADF-STEM image model assumes that the electron intensity distribution in the focused probe is the incoherent point spread function (psf) of the image.where g(x)=recorded image, f(x)=ideal image, n(x)=random noise, * represents convolution and

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3

Chen, Wei, Tingzhu Sun, Fangming Bi, Tongfeng Sun, Chaogang Tang, and Biruk Assefa. "Overview of Digital Image Restoration." Journal of New Media 1, no. 1 (2019): 35–44. http://dx.doi.org/10.32604/jnm.2019.05803.

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4

Lam, E. Y. "Image restoration in digital photography." IEEE Transactions on Consumer Electronics 49, no. 2 (May 2003): 269–74. http://dx.doi.org/10.1109/tce.2003.1209513.

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5

Chauhan, Vimal. "Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 301–13. http://dx.doi.org/10.22214/ijraset.2021.37965.

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Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

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6

Andono, Pulung Nurtantio, and Christy Atika Sari. "Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform." Scientific Journal of Informatics 9, no. 2 (November 17, 2022): 179–88. http://dx.doi.org/10.15294/sji.v9i2.34173.

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Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.

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7

Du, Hua Yue, Yong Qian, Cheng Guo Wu, Yan Hua Huang, and Xia Li. "Digital Image Restoration in Microscopic Measurement." Applied Mechanics and Materials 421 (September 2013): 421–26. http://dx.doi.org/10.4028/www.scientific.net/amm.421.421.

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The requirement of the accuracy of microscopic measurements becomes more and more high, and traditional methods have been unable to meet the demands. Digital image deconvolution techniques are applied in microscopic measurement. By fabricating a pointolite, the point spread function of the optical system is measured, and then the geometric blurring in traditional microscopy is removed by using the maximum likelihood estimation algorithms and iteration threshold segmentation algorithms. The technique is applied to measure the total content of perlite and spheroidal graphite in spheroidal graphite iron accurately and easily, and then to measure the area of a scratch scaled in 10 microns on a medical department of orthopedics plates. The technique makes great sense in the development of corresponding measurement.

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8

Amudha, J., N. Pradeepa, and R. Sudhakar. "A Survey on Digital Image Restoration." Procedia Engineering 38 (2012): 2378–82. http://dx.doi.org/10.1016/j.proeng.2012.06.284.

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9

Ding, Yongsheng, Yunbo Wei, Shuisheng Zhang, and Shihang Yu. "Digital Image Restoration Based on Multicontour Batch Scanning." Scanning 2022 (September 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/8106516.

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In order to explore the problem of digital image restoration, the authors propose a research on digital image restoration based on multicontour batch scanning. This method recommends key technical problems and solutions based on information represented by multicontour batch scans, exploring research in digital image restoration. Research has shown that the research on digital image restoration based on multicontour batch scanning is about 40% more efficient than traditional methods. Aiming at the new application of digital image inpainting, the application of image inpainting in image compression is studied in depth, and the technical principles of image inpainting and image compression are complemented.

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10

Nugroho, Ginanjar Setyo, and Gulam Hazmin. "Perbandingan Algoritma untuk Mereduksi Noise pada Citra Digital." Journal of Information Technology Ampera 3, no. 2 (August 7, 2022): 159–74. http://dx.doi.org/10.51519/journalita.volume3.isssue2.year2022.page159-174.

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Image restoration is one of the stages in the field of Digital Image Processing. Image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabillistic models of image degradation. The mathematical algorithm to reduce noise in digital images in this study uses 8 filtering algorithm methods. The purpose of this study is to compare 8 filtering algorithm and conclude which algorithm is the best for reducing noise in digital images. The method for generating noise uses Rayleigh Noise and Erlang (Gamma) Noise. The algorithm for reducing noise is Arithmetic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Median Filter, Maximum Filter, Minimum Filter, and Midpoint Filter. The measurement to determine which algorithm is the best using Root Mean Square Error (RMSE). Tests were carried out on 15 digital images by testing 1200 times. The conclusion of this study is that the best algorithm for noise reduction is Median Filter by resulting the smallest RMSE value of 6.0860942.

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11

Wu, Xue Feng, and Yu Fan. "A Research for Fuzzy Image Restoration." Advanced Materials Research 955-959 (June 2014): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1085.

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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.

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12

Wallmüller, Julia. "Adapting Classical Restoration Concepts in Moving Image Restoration and the Role of Digital Techniques." Aesthetic Investigations 2, no. 2 (July 11, 2019): 144–62. http://dx.doi.org/10.58519/aesthinv.v2i2.11965.

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Ethical and aesthetic guidelines in the field of moving image restoration have been adapted from classical restoration theory. Focusing on film but valid for moving images in general, the article provides definitions of relevant terms and discusses ethical criteria for restoration and their adaptability for moving images. Mainly the claim for authenticity in restoration opens a wide field of discussion, starting from the question about what is authentic about film. The perception of moving images and the components their aesthetic value is build upon have a great impact on restoration interventions. Digital restoration tools and digitization have gained importance during the last years. While providing a wide field of new solutions, their seemingly endless range of possibilities has lead to a revival of the discussion about ethical and aesthetic aspects in moving image restoration.

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13

Aswatha, Shashaank M., Jayanta Mukherjee, and Partha Bhowmick. "An Integrated Repainting System for Digital Restoration of Vijayanagara Murals." International Journal of Image and Graphics 16, no. 01 (January 2016): 1650005. http://dx.doi.org/10.1142/s0219467816500054.

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An integrated repainting system is proposed in this paper for digital restoration of images of heritage murals, which have historical significance in their painting styles and ritualistic contents. The repainting system uses an ensemble of conventional image processing tools, in tandem with some state-of-the-art image rendition techniques, such as scaled bilateral filtering, source-constrained inpainting, tonal processing, and texture mapping based on gradient fusion. Murals that are old by nearly four centuries, have been imaged in situ from the walls of temples under a controlled environment, and then they have been fed to our repainting system. As the work of mural art is highly subjective, and so is its interpretation, a battery of tests for subjective evaluation has been performed to compare the different stages of restoration. Three different tournament strategies have been followed to make the test result devoid of any subjective bias as far as possible. The overall evaluation result is quite encouraging, as the restored images exhibit a gradually improving quality through the different stages of restoration.

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14

Pillai, Sujita, and Sanjay Khadagade. "A Review on Digital Image Restoration Process." International Journal of Computer Applications 158, no. 7 (January 17, 2017): 40–42. http://dx.doi.org/10.5120/ijca2017912862.

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15

Abdualah Aburas, Ahmed, and Abdussalam Alhadi Addeeb. "Digital Image Restoration via Neural Networks Design." International Journal of Scientific and Research Publications (IJSRP) 11, no. 9 (September 28, 2021): 346–52. http://dx.doi.org/10.29322/ijsrp.11.09.2021.p11740.

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16

Costello, Thomas P., and Wasfy B. Mikhael. "Optical system modeling for digital image restoration." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 474. http://dx.doi.org/10.1016/s0920-5489(99)91050-4.

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17

Kikuchi, Tsutomu, Hidekazu Tsubota, Tomoyasu Tsuzuki, Hidekazu Fujii, and Tsukasa Sasaki. "99. Digital Image Restoration of High-rate Compression Degraded Images." Japanese Journal of Radiological Technology 47, no. 2 (1991): 210. http://dx.doi.org/10.6009/jjrt.kj00003322885.

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18

Li, Qiong, Yong Hang Tai, Zai Qing Chen, Qiu Yue Yang, and Bin Zhuo. "Image Pro-Correction for Defocus Blur Image Based on Wiener Filtering." Applied Mechanics and Materials 397-400 (September 2013): 2257–61. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2257.

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Image restoration is an important application of the digital image processing. Unlike traditional restoration algorithms that operate on a blurred image to recover the original, we propose a technique that the correction should be applied to the original image before blurring. To accomplish this, we approximate the Point-Spread-Function (PSF) of different defocus blur images by the circular disk. According to the estimated PSF, the original image is pro-processed based on Wiener filtering and High Dynamic Range (HDR) compression. Experiments results show that using this technique can help ameliorate the visual blur and the defocus images finally have a sharp vision.

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19

Liu, Zihan. "Literature Review on Image Restoration." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2386/1/012041.

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Abstract Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production, image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.

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20

Kim, Kwang Baek, and Doo Heon Song. "Colored facial image restoration by similarity enhanced implicative fuzzy association memory." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 199. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp199-204.

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Image restoration refers to the recovery of an underlying image from an observation that has been corrupted by various types of noise. In a digital forensic software, such image restoration process should be noise-tolerant, robust, fast, and scalable. In this paper, we apply implicative fuzzy association memory structure in colored facial image restoration with enhanced similarity measure involved in output computarion. The efficacy if the proposed fuzzy associative memory model is verified by the experiment in that it was 95% successful (with zero mean square error) out of 20 tested images.

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21

Al Maki, W. Fawwaz, T. Hori, T. Kitagawa, and S. Sugimoto. "Digital Image Restoration with Nonlinear Smoothing for Circular Motion Blurred Images." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2009 (May 5, 2009): 154–59. http://dx.doi.org/10.5687/sss.2009.154.

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22

Lei, DAI, JIANG Dai-Hong, DING Bin, and James K. Hahn. "Improved Digital Image Restoration Algorithm Based on Criminisi." Journal of Digital Information Management 14, no. 5 (October 1, 2016): 302. http://dx.doi.org/10.6025/jdim/2016/14/5/302-310.

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23

Pan, Shuai, Bo Yang, Xinru Xie, and Zhuxi Yun. "Image restoration and color fusion of digital microscopes." Applied Optics 58, no. 9 (March 13, 2019): 2183. http://dx.doi.org/10.1364/ao.58.002183.

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24

Sezan, M. Ibrahim. "Survey of recent developments in digital image restoration." Optical Engineering 29, no. 5 (1990): 393. http://dx.doi.org/10.1117/12.55610.

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25

Wen, Che-Yen, and Chiu-Chung Yu. "Fingerprint Pattern Restoration by Digital Image Processing Techniques." Journal of Forensic Sciences 48, no. 5 (September 1, 2003): 2002385. http://dx.doi.org/10.1520/jfs2002385.

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26

Barcelos, Celia A. Zorzo, and Marcos Aurélio Batista. "Image restoration using digital inpainting and noise removal." Image and Vision Computing 25, no. 1 (January 2007): 61–69. http://dx.doi.org/10.1016/j.imavis.2005.12.008.

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27

Maddalena, Lucia, and Alfredo Petrosino. "Restoration of blue scratches in digital image sequences." Image and Vision Computing 26, no. 10 (October 2008): 1314–26. http://dx.doi.org/10.1016/j.imavis.2006.04.013.

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28

Fanfani, Marco, Carlo Colombo, and Fabio Bellavia. "Restoration and Enhancement of Historical Stereo Photos." Journal of Imaging 7, no. 7 (June 24, 2021): 103. http://dx.doi.org/10.3390/jimaging7070103.

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Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.

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29

Tang, Jie, Jian Li, and Ping Tan. "Demosaicing by Differentiable Deep Restoration." Applied Sciences 11, no. 4 (February 12, 2021): 1649. http://dx.doi.org/10.3390/app11041649.

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A mosaic of color filter arrays (CFAs) is commonly used in digital cameras as a spectrally selective filter to capture color images. The captured raw image is then processed by a demosaicing algorithm to recover the full-color image. In this paper, we formulate demosaicing as a restoration problem and solve it by minimizing the difference between the input raw image and the sampled full-color result. This under-constrained minimization is then solved with a novel convolutional neural network that estimates a linear subspace for the result at local image patches. In this way, the result in an image patch is determined by a few combination coefficients of the subspace bases, which makes the minimization problem tractable. This approach further allows joint learning of the CFA and demosaicing network. We demonstrate the superior performance of the proposed method by comparing it with state-of-the-art methods in both settings of noise-free and noisy data.

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30

Masoudinejad, Sepideh, and Terry Hartig. "Window View to the Sky as a Restorative Resource for Residents in Densely Populated Cities." Environment and Behavior 52, no. 4 (October 26, 2018): 401–36. http://dx.doi.org/10.1177/0013916518807274.

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We investigated the extent to which the amount of sky and other contents affect expectations that window views will support psychological restoration in an urban context. The study involved 102 digital view images across which the amount of sky and other contents were varied systematically through manipulation of variables relevant to urban densification. University students ( N = 212) rated images on restorative quality (being away, fascination), restoration likelihood, or preference. We aggregated the ratings for each variable for each image and used the images as cases in analyses. Amount of sky and presence of a window box with greenery had direct positive effects on restoration likelihood judgments, as well as indirect effects mediated by being away and fascination. Ground-level views with people and street trees evoked ratings similar to those for some views with sky, but the views judged most restorative and most liked were those with the most sky.

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31

Govil, Anurag, David M. Pallister, and Michael D. Morris. "Three-Dimensional Digital Confocal Raman Microscopy." Applied Spectroscopy 47, no. 1 (January 1993): 75–79. http://dx.doi.org/10.1366/0003702934048497.

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We describe an iterative image restoration technique which functions as digital confocal microscopy for Raman images. We deconvolute the lateral and axial components of the microscope point spread function from a series of optical sections, to generate a stack of well-resolved Raman images which describe the three-dimensional topology of a sample. The technique provides an alternative to confocal microscopy for three-dimensional microscopic Raman imaging.

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Archana S Nadhan, Dioline Sara, Boosi Shyamala, Dr Chetana Tukkoji,. "Design a model of Image Restoration using AI in Digital Image Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 862–65. http://dx.doi.org/10.17762/turcomat.v12i5.1497.

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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.

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Hu, Yang Bo, Hua Jiang, and Long Bing Li. "The Research of Application in Image Restoration Based on Wiener Filtering." Applied Mechanics and Materials 278-280 (January 2013): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1232.

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Image, due to the impact of such as blurring, distortion, noise, etc., will cause a reduction in image quality and the formation of the degradation of the digital image. The using of some kind of a priori knowledge use the least squares method to be filtered, so that the original image and its recovery minimum mean square error between the two images. This can get a better recovery image.

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He, Jianbiao, and Changqing Li. "Research on Digital Image Intelligent Recognition Method for Industrial Internet of Things Production Data Acquisition." Traitement du Signal 39, no. 6 (December 31, 2022): 2133–39. http://dx.doi.org/10.18280/ts.390626.

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The real scene data of production images and videos collected by the perception layer of the industrial Internet of Things, which are shot under the conditions of lack of illumination, underexposure and insufficient contrast, need to be fully and efficiently utilized to ensure the smooth progress of the follow-up supervision, monitoring, detection and tracking of the industrial Internet of Things. Therefore, this paper studies the intelligent recognition method of digital images on production data collected by industrial Internet of Thing. Firstly, the video or image data collected by the industrial Internet of Things monitoring platform are preprocessed to achieve the purpose of image clarity and targeting. It includes constrained least square restoration and Lucy-Richardson restoration for image blur caused by defocus, and blind deconvolution restoration for image motion blur caused by vibration. The adaptive histogram equalization algorithm is described in detail, and it can enhance the global contrast of digital images collected by industrial Internet of Things while retaining the details of the target area as much as possible. Based on U-net convolution network, the target recognition model of digital images collected by industrial Internet of Things is constructed, and spatial convolution pooling pyramid and improved convolution module Inception are introduced to optimize the model. Experimental results verify the effectiveness of the model.

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35

Hassaïne, Abdelâali, Etienne Decencière, and Bernard Besserer. "EFFICIENT RESTORATION OF VARIABLE AREA SOUNDTRACKS." Image Analysis & Stereology 28, no. 2 (May 3, 2011): 113. http://dx.doi.org/10.5566/ias.v28.p113-119.

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The restoration of motion picture films using digital image processing has been an active research field for many years. The restoration of the soundtrack however, has mainly been performed in the sound domain, using signal processing methods, in spite of the fact that it is recorded as a continuous image between the images of the film and the perforations. In this paper a restoration method for variable area soundtrack restoration at the image level is presented. First, a novel method is proposed for the detection of the symmetry axis of the scanned soundtrack. Then, a comparison between the watershed and the region growing segmentation of the soundtrack is developed. Another algorithm aiming to enforce the symmetry and to correct the edges of the segmented image is presented. A last step aiming to smooth the edges of the obtained image is performed. Finally, experimental results are reported and possible future improvements are discussed.

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36

Arakawa, Kazuo. "Experimental Analysis of Polymerization Shrinkage of Dental Restoration Material in Cavities." Advanced Materials Research 123-125 (August 2010): 319–22. http://dx.doi.org/10.4028/www.scientific.net/amr.123-125.319.

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The polymerization shrinkage of light-cured composite resin, a dental restoration material, was studied using three different experimental methods. Digital image correlation method was used to examine the shrinkage deformation on the free surface of artificial cylindrical cavities. X-ray CT images and the digital image correlation were employed to measure the shrinkage deformation in the cavities. The shrinkage force was measured at the floor of the cavity using a load-cell, and evaluated as functions of time and the depth of the cavity.

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37

Yu, Jianjun. "Paper-Cutting Pattern Design Based on Image Restoration Technology." Security and Communication Networks 2022 (July 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/3132047.

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Paper-cutting is one of the valuable intangible cultural heritages of China, with distinctive features such as “round as the autumn moon, sharp as the wheat mane, square as the green brick, missing as the serrated teeth, and thread as the beard,” and is a widely spread folk art. The art of paper-cutting brings new inspiration to the design of structures, and by changing the topology of the raw material, the material/structure can undergo significant changes in physical properties, such as optical, thermal, acoustic, and mechanical aspects. The protection of intangible cultural heritage is a process of cultural self-awareness and a kind of cultural reflection and enlightenment. In the history of promoting socialist cultural development, the protection of “intangible heritage” paper-cutting is undoubtedly of great significance. The purpose of digital image repair is to restore the integrity of the broken image, and the process is to fill in the specified area of the digital image with information, requiring a natural transition between the filled area and the original area of the image and minimizing artificial traces, so that the image looks as if it has never been broken. In this paper, the paper-cutting pattern design method based on image restoration technology is investigated, starting from the preprocessing of paper-cutting images by acquiring the edges of the images and using the Criminisi image restoration algorithm with image broken edge reconstruction to realize the image broken edge reconstruction design of paper-cutting graphics. Therefore, the paper-cut graphics generated by the method in this paper are concise and coherent as a whole, which reduces the difficulty of paper-cut design while satisfying individual design requirements.

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Qi, Yao Long, and Zi Yu Cheng. "Linear Space-Variant Model and Restoration Algorithm of Imaging System." Applied Mechanics and Materials 608-609 (October 2014): 559–67. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.559.

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Imaging quality not only is one of the important criteria for the quality of imaging system, but also is the emphasis of designing a high-quality imaging system. For digital image processing, there are many methods of image restoration for images with noise pollution. The paper tries restoring the fundamental reason making imaging quality degraded, which is image error of lens. While reducing the design requirements of traditional optical system, the paper tries erasing aberration of optical image system, which can achieve good imaging quality.

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Peng, Zan Bin. "Application of Digital Image Processing Technology in the Process of Ceramic Art Image." Applied Mechanics and Materials 687-691 (November 2014): 3738–42. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3738.

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With the improvement of computer hardware and software performance, digital multimedia technology has been applied to various fields. The visual effect of ceramic art image is affected mainly by the brightness, texture and color, in which the image defects will affect the visual effect of the ceramic product. This paper designs the digital processing system of a new ceramic art image processing, the system can repair defects in ceramic art image. In order to verify the validity and reliability of digital processing system, the lead-free perovskite structure BZT based ceramics is prepared in this paper, and the use of electron microscope and digital camera take the ceramic products processing image with artistic defects, and then using VB programming digital image processing technology carries out color re coating for the ceramic, the ceramic products electron microscopy images after restoration will be obtained by the image boundary restructuring, which provides a new computer method for the processing art of the ceramic products.

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S, Agnes Shifani, Akshaya D, Kaviya M, and Kiruthiga K. "A Comprehensive Survey on Crack detection of Bone using various techniques." Bulletin of Scientific Research 2, no. 2 (August 19, 2020): 1–7. http://dx.doi.org/10.34256/bsr2021.

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Digital image processing plays a key role in manipulation of image and extracting the maximum amount of data from image with help of various algorithm. Digital image correlation algorithm determines the displacement and deformation of pattern across several images. Creating innovation are developing every day in various fields, particularly in restoration condition. Notwithstanding, still some old strategies are very famous. X-ray or CT images are one among the system for identification of bone cracks. during this article, we offer a comprehensive overview of various algorithm and techniques of displacement measurement generally and crack detection especially using digital image processing. we've been successful in highlighting each and each key feature and aspect of crack detection in bone which can take the add this domain further

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41

Maik, V., Dohee Cho, Jeongho Shin, and Joonki Paik. "Regularized Restoration Using Image Fusion for Digital Auto-Focusing." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 10 (October 2007): 1360–69. http://dx.doi.org/10.1109/tcsvt.2007.903776.

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Gschwind, R. "Restoration of Faded Colour Photographs by Digital Image Processing." Journal of Photographic Science 38, no. 4-5 (July 1989): 193–96. http://dx.doi.org/10.1080/00223638.1989.11737103.

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KINJO, Hiroshi, Sigeru OMATU, and Tetsuhiko YAMAMOTO. "Image Restoration in Frequency Domain Using Adaptive Digital Filters." Transactions of the Institute of Systems, Control and Information Engineers 5, no. 8 (1992): 338–45. http://dx.doi.org/10.5687/iscie.5.338.

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Kim Sang, Ku, Keun Kim Tae, and Ki Paik Joon. "Fully digital auto-focusing system based on image restoration." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 454–55. http://dx.doi.org/10.1016/s0920-5489(99)90966-2.

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45

Grabski, Varlen. "Digital Image Restoration Based on Pixel Simultaneous Detection Probabilities." IEEE Transactions on Nuclear Science 56, no. 3 (June 2009): 1389–95. http://dx.doi.org/10.1109/tns.2009.2020909.

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46

Khireddine, A., K. Benmahammed, and W. Puech. "Digital image restoration by Wiener filter in 2D case." Advances in Engineering Software 38, no. 7 (July 2007): 513–16. http://dx.doi.org/10.1016/j.advengsoft.2006.10.001.

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47

Darmawan, Dika Rizki, Fauziah Fauziah, and Ratih Titi Komalasari. "Aplikasi Perbandingan Sistem Perbaikan Citra Digital menggunakan Metode Dekonvolusi Wiener, Lucy Richardson, dan Regularized." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 4, no. 2 (November 17, 2020): 116. http://dx.doi.org/10.35870/jtik.v4i2.154.

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In some cases, there is some damage to an image caused by interference during the image capture process. Blurred image damage can be overcome by deconvolution digital image processing. There are various methods to repair the image blur damage, including using the Regularized, Wiener, and Lucy Richardson deconvolution methods. Each blurring image repair method produces a different debluring result of image processing. Image comparison application was built to compare the ability of image restoration results to a Motion Blur image with the algorithms used in deconvolution. Image restoration comparison parameters used include determining the MSE and PSNR values between the test image and the deconvolved image. The results of implementing the comparative application of Motion Blur image improvement to 270 blur simulations consisting of 9 different levels of image blurring, obtained the average PSNR value for Wiener's deconvolution = 59.16dB, Lucy Richardson = 26.92dB and Regularized = 36.94dB.Keywords:Image Restoration; Lucy Richardson; Motion Blur; Regularized; Wiener.

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Carrington, W. A., F. S. Fay, K. E. Fogarty, and L. Lifshitz. "Analysis of 3-d molecular distribution with the digital imaging microscope." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 40–41. http://dx.doi.org/10.1017/s0424820100102286.

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Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

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Kumari, Rashmi, Anupriya Asthana, and Vikas Kumar. "A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise." International Journal of Computer Vision and Image Processing 4, no. 2 (July 2014): 1–17. http://dx.doi.org/10.4018/ijcvip.2014040101.

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Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

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Yatnalli, V., B. G. Shivaleelavathi, and K. L. Sudha. "Review of Inpainting Algorithms for Wireless Communication Application." Engineering, Technology & Applied Science Research 10, no. 3 (June 7, 2020): 5790–95. http://dx.doi.org/10.48084/etasr.3547.

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Digital image inpainting is a technique of restoring large removed /damaged regions of an image with the data from the surrounding pixels of the removed region. The issue of image restoration with inpainting techniques occurs commonly in computer vision/image processing when unwanted objects have to be removed from images, for filling cracks in photographs, etc. Digital image inpainting approach is an active field of research in two significant applications of wireless communication: image compression and image recovery from a damaged image due to errors in a wireless channel. This work presents a brief survey of different image inpainting techniques and their contributions to different wireless communication applications.

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