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Journal articles on the topic 'Inpainting Algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Liu, Jiansheng, Hui Liu, Shangping Qiao, and Guangxue Yue. "An Automatic Image Inpainting Algorithm Based on FCM." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/201704.

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There are many existing image inpainting algorithms in which the repaired area should be manually determined by users. Aiming at this drawback of the traditional image inpainting algorithms, this paper proposes an automatic image inpainting algorithm which automatically identifies the repaired area by fuzzy C-mean (FCM) algorithm. FCM algorithm classifies the image pixels into a number of categories according to the similarity principle, making the similar pixels clustering into the same category as possible. According to the provided gray value of the pixels to be inpainted, we calculate the

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Sreelakshmy, I. J., and C. Kovoor Binsu. "Towards Source-Based Classification of Image Inpainting Techniques: A Survey." Journal of Information & Knowledge Management 20, no. 03 (2021): 2150039. http://dx.doi.org/10.1142/s0219649221500398.

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Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the

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Et. al., Shweta Dhondse. "Intuitionistic Fuzzy Sets Based Inpainting for Reconstruction of Heritage Images." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5556–65. http://dx.doi.org/10.17762/turcomat.v12i3.2219.

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Image inpainting is a process of reconstructing damaged or missing part of an image. Initially the work of restoration was performed by skilled artists and was limited to paintings and other artwork of eminence but this process was very tedious and strenuous and therefore digital image inpainting was introduced. The application of inpainting in reconstruction of heritage images is garnering a lot of attention from researchers. The reconstruction of heritage images using inpainting poses a challenging task because of its very high resolution and high meaning full structure content. In tradition

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Zhao, Junhong, Jintao Tan, Yaobin Huang, and Chuanlong Lu. "Improved image inpainting exemplar-based algorithms by boundary priori-knowledge." MATEC Web of Conferences 355 (2022): 03004. http://dx.doi.org/10.1051/matecconf/202235503004.

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Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, b

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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 (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 presen

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Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.

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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are

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Shen, Yankun, Yaya Su, Lin Wang, and Dongli Jia. "Research on Image Inpainting Algorithms Based on Attention Guidance." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 2 (2023): 190–97. http://dx.doi.org/10.20965/jaciii.2023.p0190.

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In recent years, the use of deep learning in image inpainting has yielded positive results. However, existing image inpainting algorithms do not pay sufficient attention to the structural and textural features of the image when inpainting, which leads to issues in the inpainting results such as blurring and distortion. To solve the above problems, a channel attention mechanism was introduced to emphasize the importance of structure and texture after extraction by the convolutional network. A bidirectional gated feature fusion module was employed to exchange and fuse the structural and textural

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Kwan, Chiman, and Jude Larkin. "Further Improvements of CFA 3.0 by Combining Inpainting and Pansharpening Techniques." Signal & Image Processing : An International Journal 11, no. 6 (2020): 1–19. http://dx.doi.org/10.5121/sipij.2020.11601.

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Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield reasonable performance as compared to some standard ones. In this paper, we investigate the use of inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one algorithm improved over other approaches.

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Song, Xiaorui, and Lingda Wu. "Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning." Applied Sciences 9, no. 15 (2019): 3062. http://dx.doi.org/10.3390/app9153062.

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To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an on

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Janardhana Rao, B., and O. Venkata Krishna. "Evaluation of Image Inpainting Algorithms." CVR Journal of Science & Technology 7, no. 1 (2014): 48–52. http://dx.doi.org/10.32377/cvrjst0708.

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Patel, Pritika, Ankit Prajapati, and Shailendra Mishra. "Review of Different Inpainting Algorithms." International Journal of Computer Applications 59, no. 18 (2012): 30–34. http://dx.doi.org/10.5120/9650-4411.

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Wang, Wenzhen, Na Deng, Binjie Xin, Chi-wai Kan, Yiliang Wang, and Shuaigang Lu. "Research on texture image inpainting of jacquard fabric based on non-single vision." Textile Research Journal 90, no. 13-14 (2019): 1462–76. http://dx.doi.org/10.1177/0040517519892924.

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For the texture image inpainting of jacquard fabric with a large damaged region but complete data information around it, traditional exemplar-based image inpainting algorithms are not only have rapid falling traditional confidence value, but also the matching precision is greatly limited due to the lack of robustness, which results in a wrong guided direction and an unsatisfactory inpainting effect. To solve the above-mentioned problems, a novel digital image acquisition system for damaged fabric was designed and one set of texture image inpainting algorithms of jacquard fabric based on non-si

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Gomathi, R., and A. Vincent Antony Kumar. "A Multiresolution Image Completion Algorithm for Compressing Digital Color Images." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/757318.

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This paper introduces a new framework for image coding that uses image inpainting method. In the proposed algorithm, the input image is subjected to image analysis to remove some of the portions purposefully. At the same time, edges are extracted from the input image and they are passed to the decoder in the compressed manner. The edges which are transmitted to decoder act as assistant information and they help inpainting process fill the missing regions at the decoder. Textural synthesis and a new shearlet inpainting scheme based on the theory ofp-Laplacian operator are proposed for image res

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Raad, Lara, Maria Oliver, Coloma Ballester, Gloria Haro, and Enric Meinhardt. "On Anisotropic Optical Flow Inpainting Algorithms." Image Processing On Line 10 (June 28, 2020): 78–104. http://dx.doi.org/10.5201/ipol.2020.281.

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Kuznetsov, A. V., and M. V. Gashnikov. "Remote sensing data retouching based on image inpainting algorithms in the forgery generation problem." Computer Optics 44, no. 5 (2020): 763–71. http://dx.doi.org/10.18287/2412-6179-co-721.

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We investigate image retouching algorithms for generating forgery Earth remote sensing data. We provide an overview of existing neural network solutions in the field of generation and inpainting of remote sensing images. To retouch Earth remote sensing data, we use imageinpainting algorithms based on convolutional neural networks and generative-adversarial neural networks. We pay special attention to a generative neural network with a separate contour prediction block that includes two series-connected generative-adversarial subnets. The first subnet inpaints contours of the image within the r

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Boscain, Ugo, Roman Chertovskih, Jean-Paul Gauthier, Dario Prandi, and Alexey Remizov. "Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion." ESAIM: Proceedings and Surveys 64 (2018): 37–53. http://dx.doi.org/10.1051/proc/201864037.

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In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information of where the image is corrupted, and others that do it. While the first algorithm is able to reconstruct only images that our visual system is still capable of recognize, we show that those of the second type completely transcend such limitation providing reconstructions at the state-of-the-art in image inpainting. This can be interpreted as a

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ARIAS, PABLO, VICENT CASELLES, GABRIELE FACCIOLO, VANEL LAZCANO, and RIDA SADEK. "NONLOCAL VARIATIONAL MODELS FOR INPAINTING AND INTERPOLATION." Mathematical Models and Methods in Applied Sciences 22, supp02 (2012): 1230003. http://dx.doi.org/10.1142/s0218202512300037.

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In this paper we study some nonlocal variational models for different image inpainting tasks. Nonlocal methods for denoising and inpainting have gained considerable attention due to their good performance on textured images, a known weakness of classical local methods which are performant in recovering the geometric structure of the image. We first review a general variational framework for the problem of nonlocal inpainting that exploits the self-similarity of natural images to copy information in a consistent way from the known parts of the image. We single out two particular methods dependi

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Chan, Tony F., Michael K. Ng, Andy C. Yau, and Andy M. Yip. "Superresolution image reconstruction using fast inpainting algorithms." Applied and Computational Harmonic Analysis 23, no. 1 (2007): 3–24. http://dx.doi.org/10.1016/j.acha.2006.09.005.

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Huang, Rong, and Yuhui Zheng. "Image Structure-Induced Semantic Pyramid Network for Inpainting." Applied Sciences 13, no. 13 (2023): 7812. http://dx.doi.org/10.3390/app13137812.

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The existing deep-learning-based image inpainting algorithms often suffer from local structure disconnections and blurring when dealing with large irregular defective images. To solve these problems, an image structure-induced semantic pyramid network for inpainting is proposed. The model consists of two parts: the edge inpainting network and the content-filling network. U-Net-based edge inpainting network restores the edge of the image defect with residual blocks. The edge inpainting map is input into the pyramid content-filling network together with the image in the prior condition. In the c

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Wang, Meiqing, Chensi Huang, Chao Zeng, and Choi-Hong Lai. "Two-Phase Image Inpainting: Combine Edge-Fitting with PDE Inpainting." Advances in Applied Mathematics and Mechanics 4, no. 06 (2012): 769–79. http://dx.doi.org/10.4208/aamm.12-12s08.

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AbstractThe digital image inpainting technology based on partial differential equations (PDEs) has become an intensive research topic over the last few years due to the mature theory and prolific numerical algorithms of PDEs. However, PDE based models are not effective when used to inpaint large missing areas of images, such as that produced by object removal. To overcome this problem, in this paper, a two-phase image inpainting method is proposed. First, some edges which cross the damaged regions are located and the missing parts of these edges are fitted by using the cubic spline interpolati

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Borole, Rajesh Pandurang, and Sanjiv Vedu Bonde. "Patch-Based Inpainting for Object Removal and Region Filling in Images." Journal of Intelligent Systems 22, no. 3 (2013): 335–50. http://dx.doi.org/10.1515/jisys-2013-0031.

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AbstractA large number of articles have been devoted to the application of “texture synthesis” for large regions and “inpainting” algorithms for small cracks in an image. A new approach that allows the simultaneous filling in of different structures and textures is discussed in this present study. The combination of structure inpainting and patch-based texture synthesis carried out (termed as “patch-based inpainting”) for filling and updating the target region shows additional advantages over earlier approaches. The algorithm discussed here uses the patch-based inpainting with isophote-driven

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Wang, Xinyi, He Wang, and Shaozhang Niu. "An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting." Mathematical Problems in Engineering 2020 (October 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8892989.

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Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning method

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Ahmed, Mariwan Wahid, and Alan Anwer Abdulla. "Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism." UHD Journal of Science and Technology 4, no. 1 (2020): 1. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp1-8.

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Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most importan

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Hayrapetyan, Sparik, Gevorg Karapetyan, Viacheslav Voronin, and Hakob Sarukhanyan. "Combination of single image super resolution and digital Inpainting algorithms based on GANS for robust image completion." Serbian Journal of Electrical Engineering 14, no. 3 (2017): 379–86. http://dx.doi.org/10.2298/sjee1703379h.

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Image inpainting, a technique of completing missing or corrupted image regions in undetected form, is an open problem in digital image processing. Inpainting of large regions using Deep Convolutional Generative Adversarial Nets (DCGAN) is a new and powerful approach. In described approaches the size of generated image and size of input image should be the same. In this paper we propose a new method where the size of input image with corrupted region can be up to 4 times larger than generated image.

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Vreja, Raluca, and Remus Brad. "Image Inpainting Methods Evaluation and Improvement." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/937845.

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With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common b

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Baranov, Mykola, Yurii Shcherbyna, and Oles Khodych. "Exploit computer vision inpainting approach to boost deep learning models." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (December 15, 2022): 1–6. http://dx.doi.org/10.23939/sisn2022.12.001.

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In today’s world, the amount of available information grows exponentially every day. Most of this data is visual data. Correspondingly, the demand for the algorithm of image rent is growing. Traditionally, the first approaches to computer vision problems were classical algorithms without the use of machine learning. Such approaches are limited by many factors. First of all, the conditions imposed on the input images are applied – the shooting angle, lighting, position of objects on the scene, etc. Other classical algorithms cannot meet the needs of modern computer vision problems. Neural netwo

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Auroux, Didier, Laurent D. Cohen, and Mohamed Masmoudi. "Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms." International Journal of Biomedical Imaging 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/592924.

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We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation) require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides a

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Sadbhawna, Vinit Jakhetiya, Badri N. Subudhi, Harshit Shakya, and Deebha Mumtaz. "Do We Need a New Large-Scale Quality Assessment Database for Generative Inpainting Based 3D View Synthesis? (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13039–40. http://dx.doi.org/10.1609/aaai.v36i11.21656.

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The advancement in Image-to-Image translation techniques using generative Deep Learning-based approaches has shown promising results for the challenging task of inpainting-based 3D view synthesis. At the same time, even the current 3D view synthesis methods often create distorted structures or blurry textures inconsistent with surrounding areas. We analyzed the recently proposed algorithms for inpainting-based 3D view synthesis and observed that these algorithms no longer produce stretching and black holes. However, the existing databases such as IETR, IRCCyN, and IVY have 3D-generated views w

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R.Patel, Kaushikkumar, Lalit Jain, and Ankurkumar G.Patel. "Image Inpainting A Review of the Underlying Different Algorithms and Comparative Study of the Inpainting Techniques." International Journal of Computer Applications 118, no. 10 (2015): 32–38. http://dx.doi.org/10.5120/20784-3422.

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Ma, Jiankang, Haoran Guo, Shenghui Rong, Junjie Feng, and Bo He. "Infrared Dim and Small Target Detection Based on Background Prediction." Remote Sensing 15, no. 15 (2023): 3749. http://dx.doi.org/10.3390/rs15153749.

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Infrared dim and small target detection is a key technology for various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ignore the global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method was proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction was

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He, Lei, Yan Xing, Kangxiong Xia, and Jieqing Tan. "An Adaptive Image Inpainting Method Based on Continued Fractions Interpolation." Discrete Dynamics in Nature and Society 2018 (June 26, 2018): 1–16. http://dx.doi.org/10.1155/2018/9801361.

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In view of the drawback of most image inpainting algorithms by which texture was not prominent, an adaptive inpainting algorithm based on continued fractions was proposed in this paper. In order to restore every damaged point, the information of known pixel points around the damaged point was used to interpolate the intensity of the damaged point. The proposed method included two steps; firstly, Thiele’s rational interpolation combined with the mask image was used to interpolate adaptively the intensities of damaged points to get an initial repaired image, and then Newton-Thiele’s rational int

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Yastrebov, V. A., A. I. Veselov, and M. R. Gilmutdinov. "Basic Methods and Principles of Developing Image Inpainting Algorithms." Informatsionno-upravliaiushchie sistemy (Information and Control Systems) 5 (November 2015): 34–42. http://dx.doi.org/10.15217/issn1684-8853.2015.5.34.

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Tavakoli, Ali, Parisa Mousavi, and Fatemeh Zarmehi. "Modified algorithms for image inpainting in Fourier transform domain." Computational and Applied Mathematics 37, no. 4 (2018): 5239–52. http://dx.doi.org/10.1007/s40314-018-0632-4.

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Seracini, Marco, and Stephen R. Brown. "Inpainting in Discrete Sobolev Spaces: Structural Information for Uncertainty Reduction." Applied Sciences 13, no. 16 (2023): 9405. http://dx.doi.org/10.3390/app13169405.

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In this article, we introduce a new mathematical functional whose minimization determines the quality of the solution for the exemplar-based inpainting-by-patch problem. The new functional expression includes finite difference terms in a similar fashion to what happens in the theoretical Sobolev spaces: its use reduces the uncertainty in the choice of the most suitable values for each point to inpaint. Moreover, we introduce a probabilistic model by which we prove that the usual principal directions, generally employed for continuous problems, are not enough to achieve consistent reconstructio

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Ji, Hui, Zuowei Shen, and Yuhong Xu. "Wavelet Based Restoration of Images with Missing or Damaged Pixels." East Asian Journal on Applied Mathematics 1, no. 2 (2011): 108–31. http://dx.doi.org/10.4208/eajam.020310.240610a.

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AbstractThis paper addresses the problem of how to restore degraded images where the pixels have been partly lost during transmission or damaged by impulsive noise. A wide range of image restoration tasks is covered in the mathematical model considered in this paper - e.g. image deblurring, image inpainting and super-resolution imaging. Based on the assumption that natural images are likely to have a sparse representation in a wavelet tight frame domain, we propose a regularization-based approach to recover degraded images, by enforcing the analysis-based sparsity prior of images in a tight fr

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Zhang, Rong, Wei Li, Peng Wang, et al. "AutoRemover: Automatic Object Removal for Autonomous Driving Videos." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12853–61. http://dx.doi.org/10.1609/aaai.v34i07.6982.

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Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage

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TIAN, Su-yun, Xiao-ming WANG, and Xue-qing ZHAO. "Image denoising algorithms based on Laplacian operator and image inpainting." Journal of Computer Applications 32, no. 10 (2013): 2793–97. http://dx.doi.org/10.3724/sp.j.1087.2012.02793.

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FLORINABEL, D. JEMI, S. EBENEZER JULIET, and V. SADASIVAM. "MULTIORIENTATION-BASED MULTISTRUCTURE MORPHOLOGICAL INPAINTING." International Journal of Image and Graphics 11, no. 02 (2011): 177–93. http://dx.doi.org/10.1142/s0219467811004056.

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A multiorientation-based multistructure morphological inpainting for the recovery of damaged digitized photographs is proposed. As the inpainting order plays a vital role for human visualization, the method is guided by the orientation of edges at the surrounding known regions of the missing (spoiled) domain. The damaged picture is decomposed into its constituent orientation subbands by steerable filters. The subband information is used for reconstructing the regions within the missing part at a particular orientation, as well as for guiding the integration of the reconstructed regions. Subban

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Almazroa, Ahmed, Sami Alodhayb, Kaamran Raahemifar, and Vasudevan Lakshminarayanan. "An Automatic Image Processing System for Glaucoma Screening." International Journal of Biomedical Imaging 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/4826385.

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Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while

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LIANG, Song, Leida LI, Bo HU, and Jianying ZHANG. "Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics." IEICE Transactions on Information and Systems E102.D, no. 7 (2019): 1430–33. http://dx.doi.org/10.1587/transinf.2018edl8206.

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Jamshidi, Ali, Mehran Yazdi, and Maryam Manafi. "Image Compression Based on Intelligent Information Removing and Inpainting Reconstruction Algorithms." Signal and Data Processing 14, no. 2 (2017): 97–114. http://dx.doi.org/10.18869/acadpub.jsdp.14.2.97.

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Xing, Mengdi, and Guorong Gao. "An efficient method to remove mixed Gaussian and random-valued impulse noise." PLOS ONE 17, no. 3 (2022): e0264793. http://dx.doi.org/10.1371/journal.pone.0264793.

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Mixed Gaussian and Random-valued impulse noise (RVIN) removal is still a big challenge in the field of image denoising. Existing denoising algorithms have defects in denoising performance and computational complexity. Based on the improved “detecting then filtering” strategy and the idea of inpainting, this paper proposes an efficient method to remove mixed Gaussian and RVIN. The proposed algorithm contains two phases: noise classification and noise removal. The noise classifier is based on Adaptive center-weighted median filter (ACWMF), three-sigma rule and extreme value processing. Different

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ARTINESCU, IRINA MARIA, and COSTIN RADU BOLDEA. ""A visual and numerical comparative study of some parallel affine projection algorithms for solving the convex feasibility problem with application to scratch inpainting"." Carpathian Journal of Mathematics 38, no. 2 (2022): 299–313. http://dx.doi.org/10.37193/cjm.2022.02.03.

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"The paper compares four variants of algorithms that solve the problem of Convex Feasibility using affine combinations of projections, two classical variants of Parallel Projection Method (PPM) and two modified variants that involve variable weight, in terms of their effectiveness in inpainting a convex polygon, as well as in terms of their convergence in a finite a number of step. We also present a numerical study of the dependence of the efficiency and the execution speed of these algorithms on the shape of the inpainted convex set, as well as on the values of the relaxation parameter."

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Imannisa Rahma, Firstyani, and Ema Utami. "Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms." Telematika 15, no. 1 (2022): 1–13. http://dx.doi.org/10.35671/telematika.v15i1.1322.

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One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in

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Hocking, L. Robert, Thomas Holding, and Carola-Bibiane Schönlieb. "Analysis of Artifacts in Shell-Based Image Inpainting: Why They Occur and How to Eliminate Them." Foundations of Computational Mathematics 20, no. 6 (2020): 1549–651. http://dx.doi.org/10.1007/s10208-020-09450-3.

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AbstractIn this paper we study a class of fast geometric image inpainting methods based on the idea of filling the inpainting domain in successive shells from its boundary inwards. Image pixels are filled by assigning them a color equal to a weighted average of their already filled neighbors. However, there is flexibility in terms of the order in which pixels are filled, the weights used for averaging, and the neighborhood that is averaged over. Varying these degrees of freedom leads to different algorithms, and indeed the literature contains several methods falling into this general class. Al

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Kim, Taeyun, and Byung Chul Yeo. "Recovering Microscopic Images in Material Science Documents by Image Inpainting." Applied Sciences 13, no. 6 (2023): 4071. http://dx.doi.org/10.3390/app13064071.

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Microscopic images in material science documents have increased in number due to the growth and common use of electron microscopy instruments. Through the use of data mining techniques, they are easily accessible and can be obtained from documents published online. As data-driven approaches are becoming increasingly common in the material science field, massively acquired experimental images through microscopy play important roles in terms of developing an artificial intelligence (AI) model for the purposes of automatically diagnosing crucial material structures. However, irrelevant objects (e

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Suwandi, Rifki, Werman Kasoep, and Ramon Luthvi Destria. "Alat Koreksi dan Rekontruksi Tulisan pada Dokumen Lama Bahasa Indonesia Berbasis Mini PC." JITCE (Journal of Information Technology and Computer Engineering) 7, no. 01 (2023): 34–39. http://dx.doi.org/10.25077/jitce.7.01.34-39.2023.

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In the digital era, preserving old documents to prevent damage is a significant challenge. One solution to this problem is to reconstruct damaged or lost documents using image processing and natural language processing technologies. This article discusses the design of a tool for correcting and reconstructing writing in old papers and documents that can be implemented on a mini PC. The tool uses state-of-the-art algorithms such as Convolutional Neural Network (CNN) for character recognition and Optical Character Recognition (OCR), as well as Image Inpainting and Sequence-to-Sequence (Seq2Seq)

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Browning, Nigel D., Jony Castagna, Angus I. Kirkland, et al. "The advantages of sub-sampling and Inpainting for scanning transmission electron microscopy." Applied Physics Letters 122, no. 5 (2023): 050501. http://dx.doi.org/10.1063/5.0135245.

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Images and spectra obtained from aberration corrected scanning transmission electron microscopes (STEM) are now used routinely to quantify the morphology, structure, composition, chemistry, bonding, and optical/electronic properties of nanostructures, interfaces, and defects in many materials/biological systems. However, obtaining quantitative and reproducible atomic resolution observations from some experiments is actually harder with these ground-breaking instrumental capabilities, as the increase in beam current from using the correctors brings with it the potential for electron beam modifi

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Su, Jian-Jia, and Chung-Hao Tien. "Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method." International Journal of Optics 2020 (July 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/3481830.

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The phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. Since the KSVD allows the content of oversampled dictionary with sparse representation to adaptively fit a given set of object examples, as long as the ER/HIO algorithms provide decent object estimation at early stage, the pixels violating the object constrai

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Treder, Kevin P., Chen Huang, Judy S. Kim, and Angus I. Kirkland. "Applications of deep learning in electron microscopy." Microscopy 71, Supplement_1 (2022): i100—i115. http://dx.doi.org/10.1093/jmicro/dfab043.

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Abstract We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified fo

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