Relevant bibliographies by topics / Image distortion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image distortion'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Image distortion"

1

Pollak, C., T. Stubbings, and H. Hutter. "Differential Image Distortion Correction." Microscopy and Microanalysis 7, no. 4 (July 2001): 335–40. http://dx.doi.org/10.1007/s10005-001-0007-1.

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AbstractImaging techniques often suffer from distortion effects. Former methods of reducing these distortions have been based either on improving the imaging technique (i.e., to avoid distortions) or on the use of reference samples (i.e., to determine the distortion field by imaging of a known structure. We present a novel method of correcting image distortion by evaluating the imaged position changes due to two small sample position shifts. The algorithm allows us to calculate a vector field, which enables us to determine the “undistorted” position of any point of the image. The presented method has very low presuppositions about the sample, requires no reference samples, and is applicable to any type of image distortion. In addition to the presentation of the method's theoretical basis and a description of the computational method, we present corrected secondary ion mass spectroscopy (SIMS) images of a regular structure (a copper grid) as well as a stochastic distribution (sodium impurities) to show the results of empirical data.
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Chen, Xiaohong, Qian Sun, and Jun Hu. "Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm." Applied Sciences 8, no. 11 (November 9, 2018): 2206. http://dx.doi.org/10.3390/app8112206.

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Radar-specific imaging geometric distortions (including foreshortening, layover, and shadow) that occur in synthetic aperture radar (SAR) images acquired over mountainous areas have a negative impact on the suitability of the interferometric SAR (InSAR) technique to monitor landslides. To address this issue, many distortion simulation methods have been presented to predict the areas in which distortions will occur before processing the SAR image. However, the layover and shadow regions are constituted by active as well as passive subregions. Since passive distortions are caused by active distortions and can occur in the flat area, it is difficult to distinguish the transition zone between passive distortion and non-distortion areas. In addition, passive distortion could cover part of the foreshortening or active layover/shadow areas but has generally been ignored. Therefore, failure to simulate passive distortion leads to incomplete simulated distortions. In this paper, an algorithm to define complete SAR geometric distortions and correct the boundaries among different distortions is presented based on the neighbor gradient between the passive and active distortions. It is an image-processing routine applied to a digital elevation model (DEM) of the terrain to be imaged by the available SAR data. The performance of the proposed method has been validated by the ascending and descending Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images acquired over the Chongqing mountainous area of China. Through the investigation of passive distortion, we can have a deeper understanding of the formation and characteristics of these distortions. Moreover, it provides very meaningful information for research on areas such as landslide monitoring.
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Ekpar, Frank, Masaaki Yoneda, and Hiroyuki Hase. "Correcting Distortions in Panoramic Images Using Constructive Neural Networks." International Journal of Neural Systems 13, no. 04 (August 2003): 239–50. http://dx.doi.org/10.1142/s0129065703001601.

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This paper presents a novel approach to the correction of panoramic (wide-angle) image distortions. Unlike traditional methods that separate the distortion of the panoramic image into radial and tangential components and then concentrate on the correction of one type of distortion at a time, the method presented in this paper uses an integrated approach that simultaneously corrects all non-linear distortions of the panoramic image. The system uses data obtained from carefully constructed calibration patterns to automatically build and train a constructive neural network of suitable complexity to approximate the characteristic distortion of the panoramic image. The trained neural network is then used to correct the distortions represented by the sample data. It is demonstrated that by applying the distortion correction method presented in this paper to panoramic images representing real world scenes, perspective-corrected views of the real world scene that are usable in a wide variety of applications can be generated.
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Asatryan, D. G., M. E. Harutyunyan, Y. I. Golub, and V. V. Starovoitov. "Influence of the distortion type on the image quality assessment when reducing its sizes." «System analysis and applied information science», no. 3 (September 25, 2020): 22–27. http://dx.doi.org/10.21122/2309-4923-2020-3-22-27.

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In this paper, the influence of various types of distortion of an image on its quality while reducing its sizes, is investigated. To assess the image quality, it is proposed to use the method of comparison with the standard using a previously developed measure based on the proximity of the values of the parameters of the Weibull distribution, which describes the gradient field of the image. The well-known TID2013 image database was used as the material, which includes 3000 images distorted by 24 types of distorting algorithms with five levels. Each image of the base was reduced by 2, 4 and 8 times by the two most common methods and compared with the original image-original. The calculations were performed for five types of distortions implemented in the database. To make a decision on the acceptability of the applied quality measure, the calculated measure values were compared with the subjective quality ratings provided along with the documentation on the TID2013 database. The comparison was carried out using Spearman’s correlation coefficient. It is shown that the average values of correlations for all images at three types of distortions are very high, while for the other two they are unacceptably low. An attempt has been made to explain this situation by the properties of distorting algorithms that change the structural properties of the image to varying degrees.The possibility of comparing images of the same scene, but with different resolutions, is demonstrated.
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Jung, Young-Hwa, Gyuho Kim, and Woo Sik Yoo. "Study on Distortion Compensation of Underwater Archaeological Images Acquired through a Fisheye Lens and Practical Suggestions for Underwater Photography - A Case of Taean Mado Shipwreck No. 1 and No. 2 -." Journal of Conservation Science 37, no. 4 (August 31, 2021): 312–21. http://dx.doi.org/10.12654/jcs.2021.37.4.01.

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Underwater archaeology relies heavily on photography and video image recording during surveillances and excavations like ordinary archaeological studies on land. All underwater images suffer poor image quality and distortions due to poor visibility, low contrast and blur, caused by differences in refractive indices of water and air, properties of selected lenses and shapes of viewports. In the Yellow Sea (between mainland China and the Korean peninsula), the visibility underwater is far less than 1 m, typically in the range of 30 cm to 50 cm, on even a clear day, due to very high turbidity. For photographing 1 m x 1 m grids underwater, a very wide view angle (180o) fisheye lens with an 8 mm focal length is intentionally used despite unwanted severe barrel-shaped image distortion, even with a dome port camera housing. It is very difficult to map wide underwater archaeological excavation sites by combining severely distorted images. Development of practical compensation methods for distorted underwater images acquired through the fisheye lens is strongly desired. In this study, the source of image distortion in underwater photography is investigated. We have identified the source of image distortion as the mismatching, in optical axis and focal points, between dome port housing and fisheye lens. A practical image distortion compensation method, using customized image processing software, was explored and verified using archived underwater excavation images for effectiveness in underwater archaeological applications. To minimize unusable area due to severe distortion after distortion compensation, practical underwater photography guidelines are suggested.
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Peng, Fu Qiang, Qiang Chen, and Jun Wei Bao. "Distortion Correction for the Gun Barrel Bore Panoramic Image." Applied Mechanics and Materials 427-429 (September 2013): 680–85. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.680.

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Single Reflector Panoramic Imaging System (SRPIS) has been widely used because of its advantages such as simple structure, fast imaging, integration and miniaturization. It can observe objects around the reflector mirror, which fits for the quality inspection of gun barrel bore. However, its images often suffer from serious distortions in radial and tangential directions. Therefore, to ensure the accuracy of captured images, distortion must be eliminated. In this paper, a distortion correction method is proposed based on the imaging characteristics of SRPIS. Firstly the relationship between the height of a certain point on the gun barrel bore and the radius of image point is derived. Then the correction model is built based on the relationship. Aiming at the captured annular image, a new chessboard corner detection algorithm is proposed. The correction parameters are obtained by using the algorithm according to the labeled image. The real experiment results demonstrate that the correction effects of radial and tangential distortions are satisfactory. The error is controlled at sub-pixel level.
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Belov, A. M., and A. Y. Denisova. "Scene distortion detection algorithm using multitemporal remote sensing images." Computer Optics 43, no. 5 (October 2019): 869–85. http://dx.doi.org/10.18287/2412-6179-2019-43-5-869-885.

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Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.
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Saifeldeen, Abdalmajeed, Shu Hong Jiao, and Wei Liu. "Entirely Blind Image Quality Assessment Estimator." Applied Mechanics and Materials 543-547 (March 2014): 2496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2496.

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Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.
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Yahanda, Alexander T., Timothy J. Goble, Peter T. Sylvester, Gretchen Lessman, Stanley Goddard, Bridget McCollough, Amar Shah, Trevor Andrews, Tammie L. S. Benzinger, and Michael R. Chicoine. "Impact of 3-Dimensional Versus 2-Dimensional Image Distortion Correction on Stereotactic Neurosurgical Navigation Image Fusion Reliability for Images Acquired With Intraoperative Magnetic Resonance Imaging." Operative Neurosurgery 19, no. 5 (June 10, 2020): 599–607. http://dx.doi.org/10.1093/ons/opaa152.

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Abstract BACKGROUND Fusion of preoperative and intraoperative magnetic resonance imaging (iMRI) studies during stereotactic navigation may be very useful for procedures such as tumor resections but can be subject to error because of image distortion. OBJECTIVE To assess the impact of 3-dimensional (3D) vs 2-dimensional (2D) image distortion correction on the accuracy of auto-merge image fusion for stereotactic neurosurgical images acquired with iMRI using a head phantom in different surgical positions. METHODS T1-weighted intraoperative images of the head phantom were obtained using 1.5T iMRI. Images were postprocessed with 2D and 3D image distortion correction. These studies were fused to T1-weighted preoperative MRI studies performed on a 1.5T diagnostic MRI. The reliability of the auto-merge fusion of these images for 2D and 3D correction techniques was assessed both manually using the stereotactic navigation system and via image analysis software. RESULTS Eight surgical positions of the head phantom were imaged with iMRI. Greater image distortion occurred with increased distance from isocenter in all 3 axes, reducing accuracy of image fusion to preoperative images. Visually reliable image fusions were accomplished in 2/8 surgical positions using 2D distortion correction and 5/8 using 3D correction. Three-dimensional correction yielded superior image registration quality as defined by higher maximum mutual information values, with improvements ranging between 2.3% and 14.3% over 2D correction. CONCLUSION Using 3D distortion correction enhanced the reliability of surgical navigation auto-merge fusion of phantom images acquired with iMRI across a wider range of head positions and may improve the accuracy of stereotactic navigation using iMRI images.
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Archip, Neculai, Olivier Clatz, Stephen Whalen, Simon P. DiMaio, Peter M. Black, Ferenc A. Jolesz, Alexandra Golby, and Simon K. Warfield. "Compensation of Geometric Distortion Effects on Intraoperative Magnetic Resonance Imaging for Enhanced Visualization in Image-guided Neurosurgery." Operative Neurosurgery 62, suppl_1 (March 1, 2008): ONS209—ONS216. http://dx.doi.org/10.1227/01.neu.0000317395.08466.e6.

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Abstract Objective: Preoperative magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, magnetic resonance spectroscopy, and positron-emission tomographic scans may be aligned to intraoperative MRI to enhance visualization and navigation during image-guided neurosurgery. However, several effects (both machine- and patient-induced distortions) lead to significant geometric distortion of intraoperative MRI. Therefore, a precise alignment of these image modalities requires correction of the geometric distortion. We propose and evaluate a novel method to compensate for the geometric distortion of intraoperative 0.5-T MRI in image-guided neurosurgery. Methods: In this initial pilot study, 11 neurosurgical procedures were prospectively enrolled. The scheme used to correct the geometric distortion is based on a nonrigid registration algorithm introduced by our group. This registration scheme uses image features to establish correspondence between images. It estimates a smooth geometric distortion compensation field by regularizing the displacements estimated at the correspondences. A patient-specific linear elastic material model is used to achieve the regularization. The geometry of intraoperative images (0.5 T) is changed so that the images match the preoperative MRI scans (3 T). Results: We compared the alignment between preoperative and intraoperative imaging using 1) only rigid registration without correction of the geometric distortion, and 2) rigid registration and compensation for the geometric distortion. We evaluated the success of the geometric distortion correction algorithm by measuring the Hausdorff distance between boundaries in the 3-T and 0.5-T MRIs after rigid registration alone and with the addition of geometric distortion correction of the 0.5-T MRI. Overall, the mean magnitude of the geometric distortion measured on the intraoperative images is 10.3 mm with a minimum of 2.91 mm and a maximum of 21.5 mm. The measured accuracy of the geometric distortion compensation algorithm is 1.93 mm. There is a statistically significant difference between the accuracy of the alignment of preoperative and intraoperative images, both with and without the correction of geometric distortion (P < 0.001). Conclusion: The major contributions of this study are 1) identification of geometric distortion of intraoperative images relative to preoperative images, 2) measurement of the geometric distortion, 3) application of nonrigid registration to compensate for geometric distortion during neurosurgery, 4) measurement of residual distortion after geometric distortion correction, and 5) phantom study to quantify geometric distortion.
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Dissertations / Theses on the topic "Image distortion"

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Kim, Younhee. "Towards lower bounds on distortion in information hiding." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3403.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 133. Thesis directors: Zoran Duric, Dana Richards. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Title from PDF t.p. (viewed Mar. 17, 2009). Includes bibliographical references (p. 127-132). Also issued in print.
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Harper, Bernard. "Body image distortion in photography." Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433029.

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Brown, Alec J. "Ipsative Score Distortion on Affinity 2.0." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1119.pdf.

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Koivula, A. (Antero). "Magnetic resonance image distortions due to artificial macroscopic objects:an example: correction of image distortion caused by an artificial hip prosthesis." Doctoral thesis, University of Oulu, 2002. http://urn.fi/urn:isbn:951426827X.

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Abstract Eddy currents and susceptibility differences are the most important sources that interfere with the quality of MR images in the presence of an artificial macroscopic object in the volume to be imaged. In this study, both of these factors have been examined. The findings show that the RF field is the most important cause of induced eddy currents when gradients with relatively slow slew rates are used. The induced eddy currents amplify or dampen the RF field with the result that the flip angle changes. At the proximal end in the vicinity of the hip prosthesis surface, there have been areas where the flip angle is nearly threefold compared to the reference flip angle. Areas with decreased flip angles have also been found near the surface of the prosthesis top. The incompleteness of the image due to eddy currents manifests as signal loss areas. Two different methods based on MRI were developed to estimate the susceptibility of a cylindrical object. One of them is based on geometrical distortions in SE magnitude images, while the other takes advantage of phase differences in GRE phase images. The estimate value of the Profile™ test hip prosthesis is χ = (170 ± 13) 10-6. A remapping method was selected to correct susceptibility image distortions. Correction was accomplished with pixel shifts in the frequency domain. The magnetic field distortions were measured using GRE phase images. The method was tested by simulations and by imaging a hip prosthesis in a water tank and in a human pelvis. The main limitations of the method described here are the loss of a single-valued correction map with higher susceptibility differences and the problems with phase unwrapping in phase images. Modulation transfer functions (MTF) were exploited to assess the effect of correction procedure. The corrected image of a prosthesis in a human hip after total hip arthroplasty appears to be equally sharp or slightly sharper than the corresponding original images. The computer programs written for this study are presented in an appendix.
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Kadaikar, Aysha-Khatoon. "Optimization of the Rate-Distortion Compromise for Stereoscopic Image Coding using Joint Entropy-Distortion Metric." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD083/document.

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Ces dernières décennies ont vu émerger de nombreuses applications utilisant la technologie 3D, telles que les écrans auto-stéréoscopiques, les écrans de télévisions 3D ou encore la visio-conférence stéréoscopique. Ces applications requièrent des techniques adaptées afin que leur flux de données soit compressé efficacement. En particulier, dans le cas des images stéréoscopiques, ces dernières étant composées de deux vues de la même scène, elles nécessitent à ce titre deux fois plus d’informations à transmettre ou à stocker que dans le cas des images 2D traditionnelles. Nos travaux se situent dans le cadre de la compression des images stéréoscopiques. Plus précisément, ils concernent l’amélioration de l’estimation de la carte de disparité dans le but d’obtenir un meilleur compromis entre le débit binaire nécessaire au codage de la carte de disparité et la qualité de l’image prédite.Généralement, la carte de disparité est estimée en minimisant la distorsion de l’image prédite. Cette minimisation peut être sujette à une contrainte de lissage. L’idée étant qu’une carte de disparité plus lisse nécessitera un débit binaire moindre en supposant que les mêmes vecteurs de disparités seront sélectionnés plus souvent. Néanmoins cette contrainte de lissage ne permet pas toujours de diminuer le coût binaire de la carte. Le lissage peut entraîner par ailleurs une augmentation notable de la distorsion de l’image prédite. Dans le premier chapitre de la thèse, nous présentons un algorithme d’estimation de carte de disparité minimisant une métrique conjointe entropie-distorsion. Le coût binaire finale de la carte de disparité est estimée à chaque étape de l’algorithme et est intégré dans le calcul de la métrique. La distorsion globale de la carte de disparité est aussi mise à jour au fur et à mesure du traitement de l’image. Par ailleurs, cette algorithme repose sur la construction séquentiel d’un arbre dont on ne garde qu’un nombre défini de branches à chaque profondeur de l’arbre. Ainsi, l’algorithme développé apporte une solution sous-optimale en minimisant le coût binaire de la carte de disparité tout en assurant une bonne qualité de l’image prédite. Le chapitre deux étend l’algorithme précédent au cas des images non rectifiées. Dans le troisième chapitre, nous nous intéressons au fait de trouver une solution au problème d’optimisation du compromis débit-distorsion en réduisant la complexité numérique par rapport à l’algorithme précédent. De ce fait, nous avons développé le R-algorithme qui se base sur une solution initiale de Référence (celle minimisant la distorsion de l’image prédite) et la modifie successivement tant qu’une amélioration est constatée en termes de compromis débit-distorsion. Le quatrième chapitre s’intéresse toujours au fait d’accroître les performances de l’algorithme développé tout en réduisant le coût en complexité numérique et donc en temps de traitement. Nous proposons deux approches afin de tirer profit d’un grand espace de recherche sans avoir à tester pour chaque bloc à apparier l’ensemble des disparités qui composent cet espace de recherche. En effet, un espace de recherche plus grand permet plus de choix de disparités et donc potentiellement une meilleur reconstruction de l’image prédite. En contrepartie, il se peut que le coût binaire de la carte de disparité augmente si l’ensemble des disparités sélectionnées constituent un ensemble plus divers qu’auparavant. Les deux approches proposées permettent de restreindre l’espace de recherche à un ensemble composées de certaines disparités permettant de minimiser la distorsion de l’image prédite pour un débit donné. Le dernier chapitre de la thèse s’intéresse à l’utilisation des blocs de taille variable pour la compression des images stéréoscopiques
During the last decades, a wide range of applications using stereoscopic technology has emerged still offering an increased immersion to the users such as video games with autostereoscopic displays, 3D-TV or stereovisio-conferencing. The raise of these applications requires fast processing and efficient compression techniques. In particular, stereoscopic images require twice the amount of information needed to transmit or store them in comparison with 2D images as they are composed of two views of the same scene. The contributions of our work are in the field of stereoscopic image compression and more precisely, we get interested in the improvement of the disparity map estimation. Generally, disparities are selected by minimizing a distortion metric which is sometimes subjected to a smoothness constraint, assuming that a smooth disparity map needs a smaller bitrate to be encoded. But a smoother disparity map does not always reduce significantly the bitrate needed to encode it but can increase the distortion of the predicted view. Therefore, the first algorithm we have proposed minimizes a joint entropy-distortion metric to select the disparities. At each step of the algorithm, the bitrate of the final disparity map is estimated and included in the metric to minimize. Moreover, this algorithm relies on a tree where a fixed number of paths are extended at each depth of the tree, ensuring good rate-distortion performance. In the second part of the work, we have proposed a sub-optimal solution with a smaller computational complexity by considering an initial solution -the one minimizing the distortion of the predicted view- which is successively modified as long as an improvement is observed in terms of rate-distortion. Then, we have studied how to take advantages of large search areas in which the disparities are selected as one can easily supposed that enlarging the search area will increase the distortion performance as there will be more choices of disparities. In the other hand, the larger is the range of the selected disparities, the higher is supposed to be the cost of the disparity map in terms of bitrate. We have proposed two approaches allowing to take advantage of a large search area by selecting only sets of disparities belonging to it enabling to achieve a given bitrate while minimizing the distortion of the predicted image. The last part of the work concerns variable block sizes which undeniably allows to improve the bitrate-distortion performance as the block size suits to the image features. We have thus proposed a novel algorithm which jointly estimates and optimizes the disparity and the block length maps
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Madsen, Jeffrey B. "Males' ipsative score distortion on Affinity 2.0 /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2479.pdf.

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Mendes, Pedro Mota. "Correction of spatial distortion in magnetic resonance imaging." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6333.

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Dissertation to Obtain the Degree of Master in Biomedical Engineering
Magnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values.
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Candan, Cagatay. "Minimum Distortion Data Hiding for Compressed Images." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5140.

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We present a novel data hiding method for compressed images. The method is designed to minimize the quality loss associated with data embedding into a JPEG image. The described technique uses the objective criterion such as the mean square error and the human visual system based criterion such as the Just Noticable Distortion metric for distortion minimization. The hiding method is designed under the restrictions of the JPEG compression standard to develop new image applications without any modifications or additions to the existing standard. An application example is presented in the thesis. The performance of the technique is examined at different image sizes and resolutions. The cost of hiding in terms of file length extension is examined. Some subjective experiments to determine the zero-perceived distortion hiding capacity are made. An application illustrating the usage of the technique is given. The described application embeds check-bits into JPEG images to facilitate the verification of the sender identity and the authenticity of the transmitted image. In this thesis, we give a list of requirements on the data hiding methods to implement standard compliant applications; design a provably good hiding method operating under these requirements; determine the critical performance points of the method and propose an application based on the method. We have performed some additional research to determine how our system works with high resolution images and existing other well-known algorithms for information hiding. The experiments on high-resolution images have shown that there exists a large embedding capacity for the high resolution images in spite of a loss of embedding density. The performance comparison experiments have shown that the spread spectrum technique offers a competitive but less efficient distortion performance.
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Tao, Yegang. "Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization." Thesis, University of Glasgow, 2005. http://theses.gla.ac.uk/4926/.

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The confocal microscopy imaging techniques, which allow optical sectioning, have been successfully exploited in biomedical studies. Biomedical scientists can benefit from more realistic visualization and much more accurate diagnosis by processing and analysing on a three-dimensional image data. The lack of efficient image compression standards makes such large volumetric image data slow to transfer over limited bandwidth networks. It also imposes large storage space requirements and high cost in archiving and maintenance. Conventional two-dimensional image coders do not take into account inter-frame correlations in three-dimensional image data. The standard multi-frame coders, like video coders, although they have good performance in capturing motion information, are not efficiently designed for coding multiple frames representing a stack of optical planes of a real object. Therefore a real three-dimensional image compression approach should be investigated. Moreover the reconstructed image quality is a very important concern in compressing medical images, because it could be directly related to the diagnosis accuracy. Most of the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete- wavelet-transform (DWT). However in DCT and DWT methods, the control of the reconstructed image quality is inconvenient, involving considerable costs in computation, since they are fundamentally rate-parameterized methods rather than distortion-parameterized methods. Therefore it is very desirable to develop a transform-based distortion-parameterized compression method, which is expected to have high coding performance and also able to conveniently and accurately control the final distortion according to the user specified quality requirement. This thesis describes our work in developing a distortion-constraint three-dimensional image compression approach, using vector quantization techniques combined with image pyramid structures. We are expecting our method to have: 1. High coding performance in compressing three-dimensional microscopic image data, compared to the state-of-the-art three-dimensional image coders and other standardized two-dimensional image coders and video coders. 2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical image compression applications, is superior to the rate-parameterized methods in achieving a user specified quality requirement. The result is a three-dimensional image compression method, which has outstanding compression performance, measured objectively, for volumetric microscopic images. The distortion-constraint feature, by which users can expect to achieve a target image quality rather than the compressed file size, offers more flexible control of the reconstructed image quality than its rate-constraint counterparts in medical image applications. Additionally, it effectively reduces the artifacts presented in other approaches at low bit rates and also attenuates noise in the pre-compressed images. Furthermore, its advantages in progressive transmission and fast decoding make it suitable for bandwidth limited tele-communications and web-based image browsing applications.
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Burcher, Michael. "A force-based method for correcting deformation in ultrasound images of the breast." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269952.

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Books on the topic "Image distortion"

1

Koide, Reiko. Body Image Deviation in Chronic Schizophrenia. New York, USA: Nova Science Publishers, 2008.

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Olsen, V. Norskov. Man, the image of God: The divine design, the human distortion. Washington, DC: Review and Herald Pub. Association, 1988.

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Schuster, Guido M. Rate-distortion based video compression: Optimal video frame compression and object boundary encoding. Boston: Kluwer Academic Publishers, 1997.

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M, Rangayyan Rangaraj, and Desautels J. E. Leo, eds. Analysis of oriented texture: With applications to the detection of architectural distortion in mammograms. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Mark, Smith A., ed. Alhacen on image-formation and distortion in mirrors: A critical edition, with English translation and commentary, of book 6 of Alhacen's De Aspectibus. Philadelphia: American Philosophical Society, 2008.

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Dailey, Denton J. Electronics for Guitarists. 2nd ed. New York, NY: Springer New York, 2013.

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The Crusades: Then and now : seeking the truth beneath the maze of distortions and image-making, using non-Muslim sources exclusively, from the birth of "revealed" religions to today's Albania, Algeria, Azerbaijan, Bosnia, Chechnya, Egypt, Iran, Iraq, Lebanon, Libya, Malaysia, Palestine, Syria, Turkey, etc. 2nd ed. Arlington, VA: First Amendment Publishers, 2004.

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Distortion of Nature's Image: Reification and the Ecological Crisis. State University of New York Press, 2019.

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Durand, Melissa A. Architectural Distortion (Cancer). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0029.

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An architectural distortion (AD) is an alteration of the breast parenchyma, which results in radiating lines or spicules emanating from a point without a distinct mass. It can occur as the primary finding, or it may be an associated feature of a mass, asymmetry, or calcifications. AD is a mammographic finding with a high positive predictive value for malignancy and is a major cause of false-negative screening exams. This chapter, appearing in the section on asymmetry, mass, and distortion, reviews the key imaging and clinical features, imaging protocols, differential diagnoses, management recommendations, and potential pitfalls for a malignant architectural distortion. Topics discussed include superimposition of breast tissue, localization, workup of tomosynthesis-detected architectural distortion, and image-guided biopsy options.
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Butler, Reni S. Architectural Distortion (Radial Scar). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0030.

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Radial scars are benign lesions of the breast characterized pathologically by a fibroelastic core containing entrapped ducts and lobules that radiate outwards in a stellate pattern. This chapter, highlighting radial scar as a cause of architectural distortion, reviews its imaging features and differential diagnosis on mammography, digital breast tomosynthesis, ultrasound, and MRI; its diagnostic workup using multiple modalities; and its histological confirmation with image-guided core needle biopsy. The particular challenge of radial scar presenting as architectural distortion seen only with tomosynthesis is discussed, along with an algorithm for imaging evaluation and biopsy guidance in this setting. As radial scar, which is histologically related to complex sclerosing lesion and radial sclerosing lesion, is considered a high-risk lesion, management recommendations are also reviewed.
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Book chapters on the topic "Image distortion"

1

Inan, Yucel. "Comparing Image Distortion of LSB." In 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018, 82–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04164-9_14.

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Mesmoudi, Mohammed Mostefa, Leila De Floriani, and Paola Magillo. "Discrete Distortion for Surface Meshes." In Image Analysis and Processing – ICIAP 2009, 652–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_70.

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Li, Shanshan, Jiang’an Wang, and Bayi Qu. "Image Scrambling Based on Linear Distortion." In Lecture Notes in Electrical Engineering, 2259–66. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_247.

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Weng, Li, and Bart Preneel. "Image Distortion Estimation by Hash Comparison." In Lecture Notes in Computer Science, 62–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17832-0_7.

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Netsch, Thomas, and Arianne van Muiswinkel. "Image Registration for Distortion Correction in Diffusion Tensor Imaging." In Biomedical Image Registration, 171–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_18.

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Gadermayr, Michael, Andreas Uhl, and Andreas Vécsei. "Distortion Adaptive Image Classification – An Alternative to Barrel-Type Distortion Correction." In Advances in Visual Computing, 465–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41939-3_45.

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Aamodt, L. C., J. C. Murphy, and J. W. Maclachlan. "Image Distortion in Optical-Beam-Deflection Imaging." In Photoacoustic and Photothermal Phenomena, 385–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-540-48181-2_101.

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Iancu, Claudia, Inge Gavat, and Mihai Datcu. "Image Disorder Characterization Based on Rate Distortion." In Current Topics in Artificial Intelligence, 261–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881216_28.

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Do, Yongtae, and Myounghwan Kim. "Learning Image Distortion Using a GMDH Network." In Advances in Neural Networks - ISNN 2006, 557–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_82.

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Bukhari, Faisal, and Matthew N. Dailey. "Robust Radial Distortion from a Single Image." In Advances in Visual Computing, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17274-8_2.

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Conference papers on the topic "Image distortion"

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Teo, Patrick C., and David J. Heeger. "Perceptual image distortion." In IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, edited by Bernice E. Rogowitz and Jan P. Allebach. SPIE, 1994. http://dx.doi.org/10.1117/12.172664.

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Kalenova, D., P. Toivanen, and V. Botchko. "Spectral image distortion map." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334257.

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Liang, Chia-Kai, Yu-Chun Peng, and Homer Chen. "Rolling shutter distortion correction." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.632671.

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Guan, Jingwei, and Wai-kuen Cham. "Distortion based image quality index." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820899.

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da Silva, Renam C., and Vanessa Testoni. "Distortion scalable learned image compression." In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2019. http://dx.doi.org/10.1109/mmsp.2019.8901769.

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Gustafson, Steven C., Vasiliki E. Nikolaou, and Farid Ahmed. "Image smoothing with minimal distortion." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Friedrich O. Huck and Richard D. Juday. SPIE, 1995. http://dx.doi.org/10.1117/12.211975.

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Zhao, Qiang, Chen Zhu, Feng Dai, Yike Ma, Guoqing Jin, and Yongdong Zhang. "Distortion-aware CNNs for Spherical Images." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/167.

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Convolutional neural networks are widely used in computer vision applications. Although they have achieved great success, these networks can not be applied to 360 spherical images directly due to varying distortion effect. In this paper, we present distortion-aware convolutional network for spherical images. For each pixel, our network samples a non-regular grid based on its distortion level, and convolves the sampled grid using square kernels shared by all pixels. The network successively approximates large image patches from different tangent planes of viewing sphere with small local sampling grids, thus improves the computational efficiency. Our method also deals with the boundary problem, which is an inherent issue for spherical images. To evaluate our method, we apply our network in spherical image classification problems based on transformed MNIST and CIFAR-10 datasets. Compared with the baseline method, our method can get much better performance. We also analyze the variants of our network.
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Bhowmik, D., and C. Abhayaratne. "Distortion constrained robustness scalable watermarking." In IET Conference on Image Processing (IPR 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0434.

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Holub, Vojtěch, and Jessica Fridrich. "Digital image steganography using universal distortion." In the first ACM workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2482513.2482514.

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Schwartzman, Armin, Marina Alterman, Rotem Zamir, and Yoav Y. Schechner. "Turbulence-induced 2D correlated image distortion." In 2017 IEEE International Conference on Computational Photography (ICCP). IEEE, 2017. http://dx.doi.org/10.1109/iccphot.2017.7951490.

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Reports on the topic "Image distortion"

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Stelmakh, Marta. HISTORICAL CONTEXT IN THE COLLECTION OF ARTICLES BY TIMOTHY SNYDER «UKRAINIAN HISTORY, RUSSIAN POLITICS, EUROPEAN FUTURE». Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11098.

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The article examines the problem of the image formation of Ukraine in the international arena in the historical journalism of Timothy Snyder. The subject of the research is the historical context in the journalistic collection «Ukrainian History, Russian Politics, European Future». It identifies the main considerations of the author on the past of Russian-Ukrainian relations and the need to develop historical consciousness in the fight against Russian manipulation. Methodology: the comparative, historical, system analysis and other methods are used in the process of scientific research. The results of the study were obtained by analysing the author’s journalistic works and by considering the main historical themes raised by Timothy Snyder. Main results: The historical context in Timothy Snyder’s journalism is often focused on the Holodomor and the events of World War II. After all, these events are connected with the beginning of the image formation of the Ukrainian people as supporters of Nazism by the Russian authorities and the devaluation of the Ukrainians’ contribution to the establishment of peace during the Second World War. It is determined that the non-reflective attitude to history, the inability to draw parallels between the events of the past and the future leads to an ineffective response to manipulation and propaganda, which can threaten world peace. Conclusions: the realization that Russian aggression against Ukraine has its own history is a necessary aspect in the elucidation of this issue. The Eurasian Union and cooperation with the European far-right are Russian propaganda tools that discredit the Ukrainian state in the world community. Publicist Timothy Snyder points out that Europe’s future interconnects with the past, so he emphasizes the need to study and rethink history, which today has become the object of propaganda and manipulation. Significance: The results of our study will help journalists who study the historical aspect of journalistic materials and research foreign materials on Ukrainian issues. In addition, our research is necessary for Ukraine, because Russia’s aggression continues, as well as the aggressor’s propaganda, which is based on the distortion and falsification of historical events.
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Rosen, David. Methods for Correcting Topographically Induced Radiometric Distortion on Landsat Thematic Mapper Images for Land Cover Classification. Portland State University Library, January 2000. http://dx.doi.org/10.15760/geogmaster.12.

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