Academic literature on the topic 'Fractal Image Processing'

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Journal articles on the topic "Fractal Image Processing"

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Kovalchuk, A. M. "THE USE OF FRACTAL CONVERTINGS AND THEIR SYSTEMS IN THE ENCRYPTION – DECRYPHATION OF MONOCHROME IMAGES." Ukrainian Journal of Information Technology 6, no. 2 (2024): 98–104. https://doi.org/10.23939/ujit2024.02.098.

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Fractals occupy a rather important and defining place in computer graphics. This is the construction of landscapes, trees, plants, even animals and the generation of fractal textures, as well as fractal image compression. Modern physics and mechanics are just beginning to study the behavior of fractal objects. And of course, fractals are used directly in mathematics itself, as well as in cryptography when protecting images. The article describes the use of elements of the RSA algorithm in fractal quadratic transformations and systems of fractal transformations for encryption / decryption of mo
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Khudaiberdiev, Mizaakbar, Igor Khan, Bobomurod Tojiboyev, and Bahodir Achilov. "Fractal representations in image processing of remote sensing of the earth." E3S Web of Conferences 541 (2024): 04010. http://dx.doi.org/10.1051/e3sconf/202454104010.

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This article explores the application of fractal representations in the image processing of remote sensing data for Earth observation. Fractals, with their self-similar properties and complex patterns, offer a powerful mathematical framework for analyzing the intricate structures found in natural landscapes. The study highlights the advantages of using fractal-based methods over traditional image processing techniques, particularly in capturing the multifaceted textures and irregularities of Earth’s surface features. By leveraging fractal geometry, enhanced accuracy in the classification and i
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Song, Juan, Bangfu Wang, Qingyang Jiang, and Xiaohong Hao. "Exploring the Role of Fractal Geometry in Engineering Image Processing Based on Similarity and Symmetry: A Review." Symmetry 16, no. 12 (2024): 1658. https://doi.org/10.3390/sym16121658.

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Fractal geometry theory has been widely used in engineering image processing. In this work, the basic principles and features of fractal geometry are first introduced and its importance in image processing is explained. The features of the symmetry and asymmetry of images are represented in fractal geometry and symmetry scaling is utilized to deal with image processing problems in engineering applications. Subsequently, specific applications of fractal geometry in engineering image processing are discussed in detail in terms of image compression, edge detection, texture analysis, and image rec
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KUKLINSKI, WALTER S. "UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING." Fractals 02, no. 03 (1994): 363–69. http://dx.doi.org/10.1142/s0218348x94000454.

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One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimi
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Xiao, Lin. "A Study on Detailed Image Processing of Fractal Characteristics of Irregular Cloud Edges." Modern Management Science & Engineering 6, no. 3 (2024): p21. http://dx.doi.org/10.22158/mmse.v6n3p21.

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With the recent development of computer graphics and image processing technology, this paper describes the progress of cloud image generation and processing techniques. Clouds in nature usually have complex and changeable shapes, so accurate and detailed processing of their images has always been an important research topic in computer graphics. First, this paper summarizes the application value of fractal theory in cloud image processing. Next, it presents the fractal generation and edge thinning algorithm as crucial technical components in forming the framework of cloud image detail processi
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Mr., Vaibhav Vijay Bulkunde, Nilesh P. Bodne Mr., and Sunil Kumar Dr. "Implementation of Fractal Image Compression on Medical Images by Different Approach." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 398–400. https://doi.org/10.31142/ijtsrd23768.

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FIC Fractal Image Processing is actually a JPG image which needs to be perform large scale encoding to improve and increase the compression ratio. In this paper we are going to analyze different constraints and algorithms of image processing in MATLAB so that there will be very low loss in image quality. It has been seen that whenever we contain any HD picture that to in medical application we need to sharpen the image that is we need to perform image encryption, image de noising and there should be no loss in image quality. In this paper, we actualized both consecutive and parallel adaptation
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CHARLES, P. D., and C. A. BROWN. "THE PATCHWORK METHOD AND IMAGE PROCESSING." Fractals 02, no. 03 (1994): 441–43. http://dx.doi.org/10.1142/s0218348x94000612.

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The patchwork method uses triangular patches to estimate surface area as a function of patch size. The patch area can be interpreted as a scale of observation. Using the patchwork method, one can identify crossover scales, or thresholds, which distinguish scales where a surface is best described by Euclidean or fractal geometries. A method is proposed for application of the patchwork method to video images of luminance intensity data. By recursively subdividing an image and applying the patchwork method, or by using a moving average and applying the patchwork method to a set of small subimages
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Dai, Guang Zhi, Guo Qiang Han, and Chao Yi Dong. "Super-Resolution Analysis on Molten Pool Image of Metal Active-Gas Welding Based on Wavelet with Fractal." Applied Mechanics and Materials 103 (September 2011): 152–57. http://dx.doi.org/10.4028/www.scientific.net/amm.103.152.

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According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively rai
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AHAMMER, H., and M. MAYRHOFER-REINHARTSHUBER. "IMAGE PYRAMIDS FOR CALCULATION OF THE BOX COUNTING DIMENSION." Fractals 20, no. 03n04 (2012): 281–93. http://dx.doi.org/10.1142/s0218348x12500260.

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The fractal dimensions of real world objects are commonly investigated using digital images. Unfortunately, these images are unable to represent an infinitesimal range of scales. In addition, a proper evaluation of the applied methods that encompass the image processing techniques is often missing. Several mathematical well-defined fractals with theoretically known fractal dimensions, represented by digital images, were investigated in this work. The very popular Box counting method was compared to a new image pyramid approach as well as to the Minkowski dilation method. Effects from noise and
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Chen, Xiang, and Jian Min Wang. "Image Segmentation Approaches of Based on Fractals." Advanced Materials Research 341-342 (September 2011): 773–75. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.773.

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Image segmentation is the key steps of image processing and analysis. In image processing process, first, we need make the image into a number of significant areas and the interested objects; this need use image segmentation technology. This paper summarizes the methods of image segmentation. Mainly the image segmentation approach of based on fractal is analyzed, and this method has received good result of image segmentation, it proved that fractals-based method of image segmentation is an effective way.
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Dissertations / Theses on the topic "Fractal Image Processing"

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Wakefield, Paul D. "Aspects of fractal image compression." Thesis, University of Bath, 1999. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285300.

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Ebrahimpour-Komleh, Hossein. "Fractal techniques for face recognition." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16289/1/Hossein_Ebrahimpour-Komleh_Thesis.pdf.

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Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recognition. In this research we present new fractal methods for recognition, especially human face recognition. This research introduces three new methods for using fractals for face recognition, the use of fractal codes directly as features, Fracta
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Ebrahimpour-Komleh, Hossein. "Fractal techniques for face recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16289/.

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Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recognition. In this research we present new fractal methods for recognition, especially human face recognition. This research introduces three new methods for using fractals for face recognition, the use of fractal codes directly as features, Fracta
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Neil, Geoffrey. "Shape recognition using fractal geometry." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319951.

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Pongratananukul, Nattorn. "Texture Segmentation Using Fractal Features." Honors in the Major Thesis, University of Central Florida, 2000. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/677.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering<br>Electrical Engineering
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McCusker, Sean. "A digital image processing approach to large-scale turbulence studies." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843989/.

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An image processing approach to turbulence studies has been developed. The approach employs a structure tracking technique to quantify the movement of coherent, large-scale turbulent structures. The 'structure tracking' technique has been applied to the shear layer of a low speed jet issuing into a low speed crossflow. A study of the characteristics of the turbulent flow within this region involved comparative measurements with hot-wire anemometry measurements within the same flow regime and fractal analysis of the flow visualisation images used by the tracking routine. Fractal analysis was ap
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Fowler, E. "Interpretation of Synthetic Aperture Radar images using fractal geometry." Thesis, Cranfield University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385750.

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Jacquin, Arnaud E. "A fractal theory of iterated Markov operators with applications to digital image coding." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/30050.

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Nolte, Ernst Hendrik. "Image compression quality measurement : a comparison of the performance of JPEG and fractal compression on satellite images." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51796.

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Thesis (MEng)--Stellenbosch University, 2000.<br>ENGLISH ABSTRACT: The purpose of this thesis is to investigate the nature of digital image compression and the calculation of the quality of the compressed images. The work is focused on greyscale images in the domain of satellite images and aerial photographs. Two compression techniques are studied in detail namely the JPEG and fractal compression methods. Implementations of both these techniques are then applied to a set of test images. The rest of this thesis is dedicated to investigating the measurement of the loss of quality that was
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Horn, Alastair N. "Iterated function systems, the parallel progressive synthesis of fractal tiling structures and their applications to computer graphics." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257907.

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Books on the topic "Fractal Image Processing"

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Barnsley, Michael. Fractal image compression. AK Peters, 1993.

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Barnsley, Michael. Fractal image compression. AK Peters, 1993.

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NATO Advanced Study Institute on Fractal Image Encoding and Analysis (1995 Trondheim, Norway). Fractal image encoding and analysis. Springer, 1998.

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NATO Advanced Study Institute on Fractal Image Encoding and Analysis (1995 Trondheim, Norway). Fractal image encoding and analysis. Springer, 1998.

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Yuval, Fisher, ed. Fractal image compression: Theory and application. Springer-Verlag, 1995.

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Ning, Lu. Fractal imaging. Academic Press, 1997.

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Li, J. M. Image-Based Fractal Description of Microstructures. Springer US, 2003.

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Colloquium on the Application of Fractal Techniques in Image Processing (1990 London). Colloquium on "The application of fractal techniques in image processing". IEE Electronics Division, 1990.

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Mishra, Jibitesh, and Soumya Ranjan Nayak. Examining Fractal Image Processing and Analysis. IGI Global, 2019.

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Mishra, Jibitesh, and Soumya Ranjan Nayak. Examining Fractal Image Processing and Analysis. IGI Global, 2019.

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Book chapters on the topic "Fractal Image Processing"

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Li, J. M., Li Lü, M. O. Lai, and B. Ralph. "Image Processing." In Image-Based Fractal Description of Microstructures. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3773-8_3.

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Lin, H., and A. N. Venetsanopoulos. "Fast fractal image coding using pyramids." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_327.

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Anh, Vo, Junji Maeda, Tohru Ishizaka, Yukinori Suzuki, and Quang Tieng. "Two-dimensional fractal segmentation of natural images." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_213.

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Popescu, Dan C., Alex Dimca, and Hong Yan. "Generalized square isometries — an improvement for fractal image coding." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_325.

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Huang, Po-Whei, Cheng-Hsiung Lee, and Phen-Lan Lin. "Classifying Pathological Prostate Images by Fractal Analysis." In Computational Intelligence in Image Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30621-1_13.

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Pinoli, Jean-Charles. "The Fractal Geometric Framework." In Mathematical Foundations of Image Processing and Analysis 2. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118984574.ch35.

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Kwiatkowski, Jan, Wieslawa Kwiatkowska, Krzysztof Kawa, and Piotr Kania. "Using Fractal Coding in Medical Image Magnification." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48086-2_57.

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Distasi, Riccardo, Michele Nappi, and Sergio Vitulano. "Speeding up fractal encoding of images using a block indexing technique." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_111.

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Backes, André R., and Odemir M. Bruno. "Plant Leaf Identification Using Multi-scale Fractal Dimension." In Image Analysis and Processing – ICIAP 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_17.

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Bilotti, Umberto, Carmen Bisogni, Michele Nappi, and Chiara Pero. "Depth Camera Face Recognition by Normalized Fractal Encodings." In Image Analysis and Processing – ICIAP 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43148-7_17.

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Conference papers on the topic "Fractal Image Processing"

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Panigrahy, Mamata, Indrajit Chakrabarti, and Anindya Sundar Dhar. "Space Limited Quadtree Based Fractal Image Encoder." In 2024 IEEE 4th International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC). IEEE, 2024. https://doi.org/10.1109/aespc63931.2024.10872333.

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Domash, Lawrence, Vincent Ryan, and Parviz Tayebati. "Optical processing of fractal images." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thqq3.

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Fractal textures occur in natural scenary or satellite images (clouds, ocean waves). However, processing of fractal image data to extract measurements such as fractal dimension is highly computation-intensive when serial digital methods are used. Massively parallel nonlinear optical processing methods are proposed to characterize fractal images in real time. A photorefractive real time image processor performing a combined convolution/correlation operation is used to measure the correlation dimension of sample fractal patterns. The optical algorithm employs an image autocorrelation performed s
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Breesam, Aqeel M., Yasmeen M. Hussein, and Mohammed J. Mohammed. "Fractal compression of digital image processing." In TRANSPORT, ECOLOGY, SUSTAINABLE DEVELOPMENT: EKO VARNA 2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0191578.

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Zhang, Zhengbing, Yaoting Zhu, Guang-Xi Zhu, Hanqiang Cao, and Donghui Xue. "Hybrid fractal image coding method." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233209.

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Lin, Huawu, and Anastasios N. Venetsanopoulos. "Perceptually lossless fractal image compression." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233214.

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Sun, Huayan, and Xiaozhong Chen. "Fractal-based image target detection." In Multispectral Image Processing and Pattern Recognition, edited by Jun Shen, Sharatchandra Pankanti, and Runsheng Wang. SPIE, 2001. http://dx.doi.org/10.1117/12.441662.

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Ratnakar, Viresh, Ephraim Feig, and Prasoon Tiwari. "Fractal-based hybrid compression schemes." In Visual Communications and Image Processing '94, edited by Aggelos K. Katsaggelos. SPIE, 1994. http://dx.doi.org/10.1117/12.185987.

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Sztojanov, Istvan, Vasilica Voinea, Justina-Lavinia Stanica, and Catalina Popescu-Mina. "Fractal technologies for image processing in biology." In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254861.

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Nelson, Susan R., and Susan M. Tuovila. "Fractal-based image processing for mine detection." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Abinash C. Dubey, Ivan Cindrich, James M. Ralston, and Kelly A. Rigano. SPIE, 1995. http://dx.doi.org/10.1117/12.211342.

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Nguyen Ky Giang and D. Saupe. "Adaptive post-processing for fractal image compression." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899258.

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