Academic literature on the topic 'Non-blind image restoration'
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Journal articles on the topic "Non-blind image restoration"
Muhson, Meryem H., and Ayad A. Al-Ani. "BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK." Iraqi Journal of Information and Communications Technology 1, no. 1 (December 15, 2021): 25–32. http://dx.doi.org/10.31987/ijict.1.1.178.
Full textHang, YANG. "A survey of non blind image restoration." Chinese Optics 15 (2022): 1–19. http://dx.doi.org/10.37188/co.2022-0099.
Full textKaram, Ghada Sabah. "Blurred Image Restoration with Unknown Point Spread Function." Al-Mustansiriyah Journal of Science 29, no. 1 (October 31, 2018): 189. http://dx.doi.org/10.23851/mjs.v29i1.335.
Full textSun, Shuhan, Lizhen Duan, Zhiyong Xu, and Jianlin Zhang. "Blind Deblurring Based on Sigmoid Function." Sensors 21, no. 10 (May 17, 2021): 3484. http://dx.doi.org/10.3390/s21103484.
Full textLiu, Qiaohong, Liping Sun, and Song Gao. "Non-convex fractional-order derivative for single image blind restoration." Applied Mathematical Modelling 102 (February 2022): 207–27. http://dx.doi.org/10.1016/j.apm.2021.09.025.
Full textZhang, Ziyu, Liangliang Zheng, Wei Xu, Tan Gao, Xiaobin Wu, and Biao Yang. "Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior." Sensors 22, no. 20 (October 16, 2022): 7858. http://dx.doi.org/10.3390/s22207858.
Full textTypke, D., R. Hegerl, and J. Kleinz. "Image restoration for biological specimens using external TEM control and electronic image recording." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 1992): 1000–1001. http://dx.doi.org/10.1017/s0424820100129632.
Full textWilliams, Bryan M., Jianping Zhang, and Ke Chen. "A new image deconvolution method with fractional regularisation." Journal of Algorithms & Computational Technology 10, no. 4 (July 28, 2016): 265–76. http://dx.doi.org/10.1177/1748301816660439.
Full textHAO Jian-kun, 郝建坤, 黄. 玮. HUANG Wei, 刘. 军. LIU Jun, and 何. 阳. HE Yang. "Review of non-blind deconvolution image restoration based on spatially-varying PSF." Chinese Optics 9, no. 1 (2016): 41–50. http://dx.doi.org/10.3788/co.20160901.0041.
Full textKuroyanagi, Shinichi, Ryota Maruo, Yukihiro Kubo, and Sueo Sugimoto. "Blind Restoration of Motion Blurred Image by Applying a Non-iterative Algorithm." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2013 (May 5, 2013): 94–100. http://dx.doi.org/10.5687/sss.2013.94.
Full textDissertations / Theses on the topic "Non-blind image restoration"
Mourya, Rahul Kumar. "Contributions to image restoration : from numerical optimization strategies to blind deconvolution and shift-variant deblurring." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES005/document.
Full textDegradations of images during the acquisition process is inevitable; images suffer from blur and noise. With advances in technologies and computational tools, the degradations in the images can be avoided or corrected up to a significant level, however, the quality of acquired images is still not adequate for many applications. This calls for the development of more sophisticated digital image restoration tools. This thesis is a contribution to image restoration. The thesis is divided into five chapters, each including a detailed discussion on different aspects of image restoration. It starts with a generic overview of imaging systems, and points out the possible degradations occurring in images with their fundamental causes. In some cases the blur can be considered stationary throughout the field-of-view, and then it can be simply modeled as convolution. However, in many practical cases, the blur varies throughout the field-of-view, and thus modeling the blur is not simple considering the accuracy and the computational effort. The first part of this thesis presents a detailed discussion on modeling of shift-variant blur and its fast approximations, and then it describes a generic image formation model. Subsequently, the thesis shows how an image restoration problem, can be seen as a Bayesian inference problem, and then how it turns into a large-scale numerical optimization problem. Thus, the second part of the thesis considers a generic optimization problem that is applicable to many domains, and then proposes a class of new optimization algorithms for solving inverse problems in imaging. The proposed algorithms are as fast as the state-of-the-art algorithms (verified by several numerical experiments), but without any hassle of parameter tuning, which is a great relief for users. The third part of the thesis presents an in depth discussion on the shift-invariant blind image deblurring problem suggesting different ways to reduce the ill-posedness of the problem, and then proposes a blind image deblurring method using an image decomposition for restoration of astronomical images. The proposed method is based on an alternating estimation approach. The restoration results on synthetic astronomical scenes are promising, suggesting that the proposed method is a good candidate for astronomical applications after certain modifications and improvements. The last part of the thesis extends the ideas of the shift-variant blur model presented in the first part. This part gives a detailed description of a flexible approximation of shift-variant blur with its implementational aspects and computational cost. This part presents a shift-variant image deblurring method with some illustrations on synthetically blurred images, and then it shows how the characteristics of shift-variant blur due to optical aberrations can be exploited for PSF estimation methods. This part describes a PSF calibration method for a simple experimental camera suffering from optical aberration, and then shows results on shift-variant image deblurring of the images captured by the same experimental camera. The results are promising, and suggest that the two steps can be used to achieve shift-variant blind image deblurring, the long-term goal of this thesis. The thesis ends with the conclusions and suggestions for future works in continuation of the current work
Marhaba, Bassel. "Restauration d'images Satellitaires par des techniques de filtrage statistique non linéaire." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0502/document.
Full textSatellite image processing is considered one of the more interesting areas in the fields of digital image processing. Satellite images are subject to be degraded due to several reasons, satellite movements, weather, scattering, and other factors. Several methods for satellite image enhancement and restoration have been studied and developed in the literature. The work presented in this thesis, is focused on satellite image restoration by nonlinear statistical filtering techniques. At the first step, we proposed a novel method to restore satellite images using a combination between blind and non-blind restoration techniques. The reason for this combination is to exploit the advantages of each technique used. In the second step, novel statistical image restoration algorithms based on nonlinear filters and the nonparametric multivariate density estimation have been proposed. The nonparametric multivariate density estimation of posterior density is used in the resampling step of the Bayesian bootstrap filter to resolve the problem of loss of diversity among the particles. Finally, we have introduced a new hybrid combination method for image restoration based on the discrete wavelet transform (DWT) and the proposed algorithms in step two, and, we have proved that the performance of the combined method is better than the performance of the DWT approach in the reduction of noise in degraded satellite images
Samarasinghe, Devanarayanage Pradeepa. "Efficient methodologies for real-time image restoration." Phd thesis, 2011. http://hdl.handle.net/1885/9859.
Full textBook chapters on the topic "Non-blind image restoration"
Satapathy, Ashutosh, and L. M. Jenila Livingston. "OpenCLTM Implementation of Rapid Image Restoration Kernels Based on Blind/Non-blind Deconvolution Techniques for Heterogeneous Parallel Systems." In Lecture Notes in Electrical Engineering, 817–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0275-7_68.
Full textAhmed, Basma, Mohamed Abdel-Nasser, Osama A. Omer, Amal Rashed, and Domenec Puig. "No-Reference Digital Image Quality Assessment Based on Structure Similarity." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210156.
Full textConference papers on the topic "Non-blind image restoration"
Samarasinghe, Pradeepa D., Rodney A. Kennedy, and Hongdong Li. "On non-blind image restoration." In 2009 3rd International Conference on Signal Processing and Communication Systems (ICSPCS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icspcs.2009.5306407.
Full textMei, Xing, Bao-Gang Hu, and Siwei Lyu. "Non-blind image restoration with symmetric generalized Pareto priors." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025908.
Full textUchida, Kazutaka, Masayuki Tanaka, and Masatoshi Okutomi. "Non-blind Image Restoration Based on Convolutional Neural Network." In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE, 2018. http://dx.doi.org/10.1109/gcce.2018.8574671.
Full textGoilkar, Suhasini S., and Dinkar M. Yadav. "Implementation of Blind and Non-blind Deconvolution for Restoration of Defocused Image." In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9397046.
Full textNagata, Takahiro, Satoshi Motohashi, Tomio Goto, and Satoshi Hirano. "Parameter adjustment of blind image restoration method by non-linear processing." In 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE). IEEE, 2017. http://dx.doi.org/10.1109/gcce.2017.8229309.
Full textLi, Sanfeng, Shijie Wang, and Limin Luo. "Study of blind image restoration algorithm based on non-negative independent component analysis." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bruce Hirsch, Zhiguo Cao, and Hanqing Lu. SPIE, 2009. http://dx.doi.org/10.1117/12.831426.
Full textMbarki, Zouhair, Hassene Seddik, and Ezzedine Ben Braiek. "Non blind image restoration scheme combining parametric wiener filtering and BM3D denoising technique." In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2018. http://dx.doi.org/10.1109/atsip.2018.8364524.
Full textQidwai, Uvais, and Chi-Hau Chen. "Blind Image Restoration for Ultrasonic C-Scan Using Constrained 4th Order Cumulants." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/nde-25812.
Full textRehman, Atta Ur, Aftab Khan, Ashfaq Khan, Sulaiman Khan, and Safdar Nawaz Khan Marwat. "A dialectical analysis of non-reference image quality measures (IQMs) and restoration filters for single image blind deblurring." In 2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT). IEEE, 2016. http://dx.doi.org/10.1109/kacstit.2016.7756064.
Full textSheer, Alaa H., and Ayad A. Al-Ani. "The Effect of Regularization Parameter within Non-blind Restoration Algorithm Using Modified Iterative Wiener Filter for Medical Image." In 2018 1st Annual International Conference on Information and Sciences (AiCIS). IEEE, 2018. http://dx.doi.org/10.1109/aicis.2018.00026.
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