Relevant bibliographies by topics / Image restoration

Academic literature on the topic 'Image restoration'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Image restoration"

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Chan, A., and J. Meloche. "Image restoration." Journal of Statistical Planning and Inference 65, no. 2 (December 1997): 233–54. http://dx.doi.org/10.1016/s0378-3758(97)00066-9.

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Qiu, Peihua. "Image restoration." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 110–13. http://dx.doi.org/10.1002/wics.7.

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Gupta, Nakul Kumar, and Dr S. K. Manju Bargavi. "Image Restoration." International Journal of Innovative Research in Information Security 10, no. 02 (February 10, 2024): 42–50. http://dx.doi.org/10.26562/ijiris.2023.v1002.01.

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Image restoration is an integral component of computer vision that tries to restore pictures that have been deteriorated or corrupted to their original or enhanced condition. In this study, we look into the wide-ranging terrain of picture restoration techniques, which includes both conventional filter-based approaches and cutting-edge deep learning models. There are certain circumstances in which traditional approaches, such as Wiener filtering and bilateral filtering, perform quite well, particularly when it comes to smoothing and noise reduction. On the other hand, the fact that they rely on handcrafted filters restricts their adaptation to more complicated forms of degradation. Visual restoration has been revolutionized by deep learning, which is led by convolutional neural networks (CNNs). Deep learning involves learning sophisticated representations of visual data. It is because of this that CNNs are able to deal with a wide variety of degradations, such as noise, blurring, artifacts, and missing data. Generative adversarial networks, often known as GANs, are continually pushing the limits of what is possible by utilizing adversarial training to accomplish spectacular outcomes in the areas of in painting and picture super-resolution. Despite amazing development, there are still obstacles to overcome: Understanding the inner workings of deep learning models continues to be a challenge, thanks to the limited interpretability of the data. Dependence on data: Acquiring large quantities of high-quality data is necessary for the training of successful models. Costs associated with computation: The process of training and deploying deep learning models may be quite computationally rigorous. The improvement of camera vision for autonomous cars in order to make navigation safer and more dependable overall. Image restoration technology has the potential to continue to revolutionize image processing and analysis, ultimately contributing to advancements across a wide range of scientific and technological domains. This can be accomplished by addressing the challenges that are currently being faced and concentrating on the promising research directions that are currently being pursued.
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Shah, Ankur N., and Dr K. H. Wandra Dr. K. H. Wandra. "Introduction to noise, image restoration and comparison of various methods of image restoration by removing noise from image." Indian Journal of Applied Research 2, no. 1 (October 1, 2011): 67–68. http://dx.doi.org/10.15373/2249555x/oct2012/22.

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Rathee, S., Z. J. Koles, and T. R. Overton. "Image restoration in computed tomography: restoration of experimental CT images." IEEE Transactions on Medical Imaging 11, no. 4 (1992): 546–53. http://dx.doi.org/10.1109/42.192690.

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Wu, Xue Feng, and Yu Fan. "A Research on the Optimization of Fuzzy Image." Applied Mechanics and Materials 409-410 (September 2013): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1593.

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The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.
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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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Wieslander, Håkan, Carolina Wählby, and Ida-Maria Sintorn. "TEM image restoration from fast image streams." PLOS ONE 16, no. 2 (February 1, 2021): e0246336. http://dx.doi.org/10.1371/journal.pone.0246336.

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Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.
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Fan, Yu, and Xue Feng Wu. "Study on Motion Blur Image Restoration Algorithms." Advanced Materials Research 753-755 (August 2013): 2976–79. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2976.

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The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.
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Tahir KASIM, Ghada Mohammad, Zahraa Mazin ALKATTAN, and Nadia Maan MOHAMMED. "Hybrid System for Image Restoration." International Research Journal of Innovations in Engineering and Technology 08, no. 01 (2024): 168–77. http://dx.doi.org/10.47001/irjiet/2024.801020.

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The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results.
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Dissertations / Theses on the topic "Image restoration"

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Ungan, Cahit Ugur. "Nonlinear Image Restoration." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606796/index.pdf.

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This thesis analyzes the process of deblurring of degraded images generated by space-variant nonlinear image systems with Gaussian observation noise. The restoration of blurred images is performed by using two methods
a modified version of the Optimum Decoding Based Smoothing Algorithm and the Bootstrap Filter Algorithm which is a version of Particle Filtering methods. A computer software called MATLAB is used for performing the simulations of image estimation. The results of some simulations for various observation and image models are presented.
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Dolne, Jean J. "Estimation theoretical image restoration." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47859.

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Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.
Includes bibliographical references.
In this thesis, we have developed an extensive study to evaluate image restoration from a single image, colored or monochromatic. Using a mixture of Gaussian and Poisson noise process, we derived an objective function to estimate the unknown object and point spread function (psf) parameters. We have found that, without constraint enforcement, this blind deconvolution algorithm tended to converge to the trivial solution: delta function as the estimated psf and the detected image as the estimated object. We were able to avoid this solution set by enforcing a priori knowledge about the characteristics of the solution, which included the constraints on object sharpness, energy conservation, impulse response point spread function solution, and object gradient statistics. Applying theses constraints resulted in significantly improved solutions, as evaluated visually and quantitatively using the distance of the estimated to the true function. We have found that the distance of the estimated psf was correlated better with visual observation than the distance metric using the estimated object. Further research needs to be done in this area. To better pose the problem, we expressed the point spread function as a series of Gaussian basis functions, instead of the pixel basis function formalism used above. This procedure has reduced the dimensionality of the parameter space and has resulted in improved results, as expected. We determined a set of weights that yielded optimum algorithm performance.
(cont.) Additional research needs to be done to include the weight set as optimization parameters. This will free the user from having to adjust the weights manually. Of course, if certain knowledge of a weight is available, then it may be better to start with that as an initial guess and optimize from there. With the knowledge that the gradient of the object obeys long-tailed distribution, we have incorporated a constraint using the first two moments, mean and variance, of the gradient of the object in the objective function. Additional research should be done to incorporate the entire distribution in the objective and gradient functions and evaluate the performance.
by Jean J. Dolne.
S.M.
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Pai, Hung-ta. "Multichannel blind image restoration /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Reichenbach, Stephen Edward. "Small-kernel image restoration." W&M ScholarWorks, 1989. https://scholarworks.wm.edu/etd/1539623783.

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The goal of image restoration is to remove degradations that are introduced during image acquisition and display. Although image restoration is a difficult task that requires considerable computation, in many applications the processing must be performed significantly faster than is possible with traditional algorithms implemented on conventional serial architectures. as demonstrated in this dissertation, digital image restoration can be efficiently implemented by convolving an image with a small kernel. Small-kernel convolution is a local operation that requires relatively little processing and can be easily implemented in parallel. A small-kernel technique must compromise effectiveness for efficiency, but if the kernel values are well-chosen, small-kernel restoration can be very effective.;This dissertation develops a small-kernel image restoration algorithm that minimizes expected mean-square restoration error. The derivation of the mean-square-optimal small kernel parallels that of the Wiener filter, but accounts for explicit spatial constraints on the kernel. This development is thorough and rigorous, but conceptually straightforward: the mean-square-optimal kernel is conditioned only on a comprehensive end-to-end model of the imaging process and spatial constraints on the kernel. The end-to-end digital imaging system model accounts for the scene, acquisition blur, sampling, noise, and display reconstruction. The determination of kernel values is directly conditioned on the specific size and shape of the kernel. Experiments presented in this dissertation demonstrate that small-kernel image restoration requires significantly less computation than a state-of-the-art implementation of the Wiener filter yet the optimal small-kernel yields comparable restored images.;The mean-square-optimal small-kernel algorithm and most other image restoration algorithms require a characterization of the image acquisition device (i.e., an estimate of the device's point spread function or optical transfer function). This dissertation describes an original method for accurately determining this characterization. The method extends the traditional knife-edge technique to explicitly deal with fundamental sampled system considerations of aliasing and sample/scene phase. Results for both simulated and real imaging systems demonstrate the accuracy of the method.
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Boukouvala, Erisso. "Image restoration techniques and application on astronomical images." Thesis, University of Reading, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414571.

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Katsaggelos, Aggelos Konstantinos. "Constrained iterative image restoration algorithms." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/15830.

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Huang, Yumei. "Numerical methods for image restoration." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/908.

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Yan, Ruomei. "Adaptive representations for image restoration." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6975/.

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In the field of image processing, building good representation models for natural images is crucial for various applications, such as image restoration, sampling, segmentation, etc. Adaptive image representation models are designed for describing the intrinsic structures of natural images. In the classical Bayesian inference, this representation is often known as the prior of the intensity distribution of the input image. Early image priors have forms such as total variation norm, Markov Random Fields (MRF), and wavelets. Recently, image priors obtained from machine learning techniques tend to be more adaptive, which aims at capturing the natural image models via learning from larger databases. In this thesis, we study adaptive representations of natural images for image restoration. The purpose of image restoration is to remove the artifacts which degrade an image. The degradation comes in many forms such as image blurs, noises, and artifacts from the codec. Take image denoising for an example. There are several classic representation methods which can generate state-of-the-art results. The first one is the assumption of image self-similarity. However, this representation has the issue that sometimes the self-similarity assumption would fail because of high noise levels or unique image contents. The second one is the wavelet based nonlocal representation, which also has a problem in that the fixed basis function is not adaptive enough for any arbitrary type of input images. The third is the sparse coding using over-complete dictionaries, which does not have the hierarchical structure that is similar to the one in human visual system and is therefore prone to denoising artifacts. My research started from image denoising. Through the thorough review and evaluation of state-of-the-art denoising methods, it was found that the representation of images is substantially important for the denoising technique. At the same time, an improvement on one of the nonlocal denoising methods was proposed, which improves the representation of images by the integration of Gaussian blur, clustering and Rotationally Invariant Block Matching. Enlightened by the successful application of sparse coding in compressive sensing, we exploited the image self-similarity by using a sparse representation based on wavelet coefficients in a nonlocal and hierarchical way, which generates competitive results compared to the state-of-the-art denoising algorithms. Meanwhile, another adaptive local filter learned by Genetic Programming (GP) was proposed for efficient image denoising. In this work, we employed GP to find the optimal representations for local image patches through training on massive datasets, which yields competitive results compared to state-of-the-art local denoising filters. After successfully dealing with the denoising part, we moved to the parameter estimation for image degradation models. For instance, image blur identification uses deep learning, which has recently been proposed as a popular image representation approach. This work has also been extended to blur estimation based on the fact that the second step of the framework has been replaced with general regression neural network. In a word, in this thesis, spatial correlations, sparse coding, genetic programming, deep learning are explored as adaptive image representation models for both image restoration and parameter estimation. We conclude this thesis by considering methods based on machine learning to be the best adaptive representations for natural images. We have shown that they can generate better results than conventional representation models for the tasks of image denoising and deblurring.
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Sandor, Viviana. "Wavelet-based digital image restoration." W&M ScholarWorks, 1998. https://scholarworks.wm.edu/etd/1539623937.

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Digital image restoration is a fundamental image processing problem with underlying physical motivations. A digital imaging system is unable to generate a continuum of ideal pointwise measurements of the input scene. Instead, the acquired digital image is an array of measured values. Generally, algorithms can be developed to remove a significant part of the error associated with these measure image values provided a proper model of the image acquisition system is used as the basis for the algorithm development. The continuous/discrete/continuous (C/D/C) model has proven to be a better alternative compared to the relatively incomplete image acquisition models commonly used in image restoration. Because it is more comprehensive, the C/D/C model offers a basis for developing significantly better restoration filters. The C/D/C model uses Fourier domain techniques to account for system blur at the image formation level, for the potentially important effects of aliasing, for additive noise and for blur at the image reconstruction level.;This dissertation develops a wavelet-based representation for the C/D/C model, including a theoretical treatment of convolution and sampling. This wavelet-based C/D/C model representation is used to formulate the image restoration problem as a generalized least squares problem. The use of wavelets discretizes the image acquisition kernel, and in this way the image restoration problem is also discrete. The generalized least squares problem is solved using the singular value decomposition. Because image restoration is only meaningful in the presence of noise, restoration solutions must deal with the issue of noise amplification. In this dissertation the treatment of noise is addressed with a restoration parameter related to the singular values of the discrete image acquisition kernel. The restoration procedure is assessed using simulated scenes and real scenes with various degrees of smoothness, in the presence of noise. All these scenes are restoration-challenging because they have a considerable amount of spatial detail at small scale. An empirical procedure that provides a good initial guess of the restoration parameter is devised.
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Ahtaiba, Ahmed Mohamed A. "Restoration of AFM images using digital signal and image processing." Thesis, Liverpool John Moores University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604322.

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All atomic force microscope (AFM) images suffer from distortions, which are principally produced by the interaction between the measured sample and the AFM tip. If the three-dimensional shape of the tip is known, the distorted image can be processed and the original surface form ' restored' typically by deconvolution approaches. This restored image gives a better representation of the real 3D surface or the measured sample than the original distorted image. In this thesis, a quantitative investigation of using morphological deconvolution has been used to restore AFM images via computer simulation using various computer simulated tips and objects. This thesis also presents the systematic quantitative study of the blind tip estimation algorithm via computer simulation using various computer simulated tips and objects. This thesis proposes a new method for estimating the impulse response of the AFM by measuring a micro-cylinder with a-priori known dimensions using contact mode AFM. The estimated impulse response is then used to restore subsequent AFM images, when measured with the same tip, under similar measurement conditions. Significantly, an approximation to what corresponds to the impulse response of the AFM can be deduced using this method. The suitability of this novel approach for restoring AFM images has been confirmed using both computer simulation and also with real experimental AFM images. This thesis suggests another new approach (impulse response technique) to estimate the impulse response of the AFM. this time from a square pillar sample that is measured using contact mode AFM. Once the impulse response is known, a deconvolution process is carried out between the estimated impulse response and typical 'distorted' raw AFM images in order to reduce the distortion effects. The experimental results and the computer simulations validate the performance of the proposed approach, in which it illustrates that the AFM image accuracy has been significantly improved. A new approach has been implemented in this research programme for the restoration of AFM images enabling a combination of cantilever and feedback signals at different scanning speeds. In this approach, the AFM topographic image is constructed using values obtained by summing the height image that is used for driving the Z-scanner and the deflection image with a weight function oc that is close to 3. The value of oc has been determined experimentally using tri al and error. This method has been tested 3t ten different scanning speeds and it consistently gives more faithful topographic images than the original AFM images.
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Books on the topic "Image restoration"

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Katsaggelos, Aggelos K., ed. Digital Image Restoration. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58216-5.

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1956-, Katsaggelos Aggelos Konstantinos, ed. Digital image restoration. Berlin: Springer-Verlag, 1991.

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J, McDonnell M., ed. Image restoration and reconstruction. Oxford [Oxfordshire]: Clarendon Press, 1986.

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Hunter, Michael. The Image of Restoration Science. London ; New York : Routledge, 2017.: Routledge, 2016. http://dx.doi.org/10.4324/9781315556857.

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Image restoration: Fundamentals and advances. Boca Raton, FL: CRC Press, 2012.

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1958-, Sezan M. Ibrahim, ed. Selected papers on digital image restoration. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1992.

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Jan, Biemond, ed. Iterative identification and restoration of images. Boston: Kluwer Academic Publishers, 1991.

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Lemeshewsky, George. Iterative restoration deblurring of SPOT panchromatic images. Reston, VA: U.S. Geological Survey, 1993.

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J, Schulz Timothy, and Society of Photo-optical Instrumentation Engineers., eds. Image reconstruction and restoration II: 28-29 July 1997, San Diego, California. Bellingham, Wash., USA: SPIE, 1997.

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Moayeri, Nader. An algorithm for blind restoration of blurred and noisy images. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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Book chapters on the topic "Image restoration"

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Beyerer, Jürgen, Fernando Puente León, and Christian Frese. "Image Restoration." In Machine Vision, 521–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47794-6_10.

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Cornwell, T. J. "Image Restoration." In Diffraction-Limited Imaging with Very Large Telescopes, 273–92. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2340-9_16.

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Vyas, Aparna, Soohwan Yu, and Joonki Paik. "Image Restoration." In Signals and Communication Technology, 133–98. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7272-7_5.

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Osher, Stanley, and Ronald Fedkiw. "Image Restoration." In Applied Mathematical Sciences, 97–118. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-22746-6_11.

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Sundararajan, D. "Image Restoration." In Digital Image Processing, 143–61. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_5.

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Weik, Martin H. "image restoration." In Computer Science and Communications Dictionary, 753. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8660.

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Thanki, Rohit M., and Ashish M. Kothari. "Image Restoration." In Digital Image Processing using SCILAB, 71–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89533-8_4.

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Moura Neto, Francisco Duarte, and Antônio José da Silva Neto. "Image Restoration." In An Introduction to Inverse Problems with Applications, 85–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32557-1_5.

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Zhou, Yi-Tong, and Rama Chellappa. "Image Restoration." In Artificial Neural Networks for Computer Vision, 122–46. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2834-9_7.

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Jan, Jiří. "Image Restoration." In Medical Image Processing, Reconstruction and Analysis, 395–434. Other titles: Medical image processing, reconstruction, and restoration Description: Second edition. | Boca Raton: CRC Press, 2019. | Preceded by Medical image processing, reconstruction, and restoration/Jiří Jan.2006.: CRC Press, 2019. http://dx.doi.org/10.1201/b22391-15.

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Conference papers on the topic "Image restoration"

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Mammone, R. J., and R. J. Rothacker. "Two dimensional image restoration using Linear Programming." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/srs.1986.wa3.

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In this paper we address the issues involved in implementing the Linear Programming (LP) method of image restoration in two dimensions. A modified pivot strategy is introduced in order to reduce the number of iterations. This approach is necessary due to the number of arithmetic operations required per iteration for two dimensional data. We shall also discuss the effects of additive noise, such as that due to quantization. The effects of noise on the performance of restorations with both separable and nonseparable degradations will be presented. The performance of the LP approach has previously been seen to show a preference for sparse images C13, i.e. images with many zero valued pixels. This preference is also found in the two dimensional case. The error in the LP restored image is shown to be less for sparse images than it is for dense images with the same signal-to-noise ratio (SNR). Linear Programming, despite its name, is nonlinear. That is, the LP solution of the sum of two images is not necessarily the sum of the two solutions obtained for each image separately. The LP method also allows for inequality as well as equality constraints to be imposed on the solution. The advantage in performance of the constrained nonlinear approach taken here over linear non-constrained methods is also demonstrated. This is accompli shed by illustrating the pseudo-inverse solution for each LP restoration. The pseudo-inverse method represents the optimal non-constrained linear restoration.
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Saleh, Bahaa E. A., and R. K. Ward. "Image restoration in random time-varying systems." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.thq7.

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The problem of restoring a constant image distorted by a system of random time-varying impulse response is discussed. The restoration is based on the observed time-varying distorted image during a finite period of time T. Three methods are considered. Restorations based on the average image is considered first. If the observation time T is finite, the system noise remains to play a role. This results in a restoration problem with object-dependent noise. Second, restoration based on the averaged spatial correlation of the image will be introduced. For finite T the result is an image restoration problem which we could not solve. Third, restoration based on a finite number of image frames will be examined in detail. We use an iterative method based on the minimum-variance unbiased estimation. When the frames are uncorrelated, we obtain the result based on using the average image as a statistic. When the frames are correlated but space–time separable, a Karhunen-Loève transformation that decorrelates the frames is used to reduce the computations. Using an example, we show how the restoration error drops as more frames are included. The higher the interframe correlation, the more reduction of restoration error is obtained by using the frame data, compared with the error of restoration based on frame averaging.
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Walkup, John F. "Image restoration in signal-dependent noise." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.tha2.

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Image restoration problems are commonly complicated by the presence of signal-dependent noise (SDN) sources (e.g., film grain noise, photoelectronic shot noise, speckle). As a consequence, both optimal and suboptimal restoration techniques are frequently nonlinear. This paper presents a review of research on techniques for using SDN models in image processing. Topics discussed include (1) noise source modeling; (2) nonlinear optimal/suboptimal restoration in SDN; (3) image recovery from SDN only; (4) adaptive point restoration; (5) restoration using a Markovian covariance model; (6) robust restoration techniques; and (7) restoration based on an initial transformation of SDN to SIN (signal-independent noise). Both analytical results and the results of computer simulations on various test images are presented.
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Kochher, Rajesh, Anshu Oberoi, and Pallavi Goel. "Image restoration on mammography images." In 2016 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2016. http://dx.doi.org/10.1109/ccaa.2016.7813894.

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Fiddy, M. A. "Quantum-limited image restoration." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ft2.

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Imaging at low-light levels relies on intensity measurements that have a signal-to-noise ratio of N , if N photons are detected at a pixel. Usually, a simple time integration to improve the SNR is not possible because the object being imaged or the measurement system is moving; this results in a loss of high spatial frequency information and possible blur. Examples of imaging techniques are described along with the usual techniques for dealing with the photon-limited data, poor SNR, and blur. Correlation based methods (recovery from Fourier magnitude or bispectral data) provide a solution to some of these problems, in principle, and are outlined. Spectral estimation techniques to restore high spatial frequencies or missing phase information can further improve these images but are very noise sensitive in general. New methods are described that are either explicitly or implicitly regularized and permit reliable image restoration; they are based on minimum norm estimators that can incorporate prior knowledge about the object, if available. Both linear and nonlinear estimators can be defined and the role of the prior knowledge is quite different in the two cases.
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Rabbani, Majid. "Restoration Techniques for Quantum-Limited Images." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.mc2.

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Stojancic, M., and G. Eichmann. "Superresolving image restoration using an associative memory processor." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wt10.

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The issue of superresolving signal and image restoration appears in many areas, such as the restoration of diffraction-limited images, geophysical prospecting, radar and sonar signal processing, biomedical imaging, image and signal bandwidth compression, reconstruction of signals and images from partial and incomplete information, etc. There is a large repertoire of different techniques and procedures available to reconstruct such images. However, most of these techniques work well only in the absence of noise. Because this is an ill-posed problem, the actual reconstruction is sensitive to noise. Recently, new optical signal and image processing techniques have been suggested that are based on associative memory processing concepts. The purpose of this paper is to explore various associative memory processor concepts applied to the superresolving image restoration problem. Results of computer simulation will be presented.
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Hong Sun, H. Maitre, and Bao Guan. "Turbo image restoration." In Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isspa.2003.1224729.

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Kasturi, Rangachar, and John F. Walkup. "Nonlinear Image Restoration." In 1985 Los Angeles Technical Symposium, edited by Andrew G. Tescher. SPIE, 1985. http://dx.doi.org/10.1117/12.946406.

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Gladkova, Irina, Michael Grossberg, and Fazlul Shahriar. "Quantitative image restoration." In SPIE Defense, Security, and Sensing, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2010. http://dx.doi.org/10.1117/12.851731.

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Reports on the topic "Image restoration"

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Jennison, Christopher, and Michael Jubb. Statistical Image Restoration and Refinement. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada196142.

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Murphy, P. K. Survey of Image Restoration Techniques. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197470.

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Chan, Tony F., and Jianhong Shen. A Good Image Model Eases Restoration. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada437474.

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Goda, Matthew E. Wavelet Domain Image Restoration and Super-Resolution. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada405111.

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Mairal, Julien, Michael Elad, and Guillermo Sapiro. Sparse Representation for Color Image Restoration (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada478437.

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Carasso, Alfred S., and András E. Vladár. Calibrating image roughness by estimating Lipschitz exponents, with application to image restoration. Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7438.

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Jefferies, Stuart M., Douglas A. Hope, and C. A. Giebink. Next Generation Image Restoration for Space Situational Awareness. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada495284.

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Lal, Anisha M., Ali A. Abdulla, and Aju Dennisan. Remote Sensing Image Restoration for Environmental Applications Using Estimated Parameters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2018. http://dx.doi.org/10.7546/crabs.2018.08.11.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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