Relevant bibliographies by topics / Image enhancement

Contents

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

Academic literature on the topic 'Image enhancement'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Image enhancement"

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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and c
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B., Mrs Rajeswari. "Night Time Image Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29951.

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Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to lear
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Suryamani, Singh*1 &. Mrs. Priyanka Gaur2. "REVIEW PAPER ON UNDERWATER AND SATELLITE IMAGE ENHANCEMENT USING AUTO THRESHOLD METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 429–34. https://doi.org/10.5281/zenodo.1247299.

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Now days applications require various kinds of images as sources of information for interpretation and inspection. Image enhancement is method of applying different alterations to an input image to make the resultant image more pleasing or to provide a better transform presentation for future automated image processing techniques. Many images like medical images, images of satellites, and even real life photographs suffer from poor sharpness and noisy effects. This is essential to enhance the contrast and remove the noise to increase picture standard. One is presenting a review on various imag
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Prateeshwaran, P., Dr N. Keerthana, and Dr S. Kevin Andrews. "Underwater Image Enhancement Techniques." International Journal of Research Publication and Reviews 5, no. 4 (2024): 6148–55. http://dx.doi.org/10.55248/gengpi.5.0424.1129.

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Mohamed Y Abdallah, Yousif, Mohamed MO Yousef, and Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 557–64. https://doi.org/10.21275/sr21709185141.

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Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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Syed, Nazeeburrehman, and Ali Hussain Mohameed. "Image Resolution Enhancement Using Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 354–56. https://doi.org/10.11591/ijeecs.v9.i2.pp354-356.

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In this project, interruption based image resolution enhancement technique using Discrete Wavelet Transform (DWT) with high-frequency sub bands obtained is proposed. Input images are decomposed by using DWT in this proposed enhancement technique. Inverse DWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio
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Suralkar, S. R., and Seema Rajput. "Enhancement of Images Using Contrast Image Enhancement Techniques." International Journal Of Recent Advances in Engineering & Technology 08, no. 03 (2020): 16–20. http://dx.doi.org/10.46564/ijraet.2020.v08i03.004.

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Mrs., Shital G. More, and L.K.Chouthmol Prof. "IMAGE ENHANCEMENT FOR SATELLITE IMAGE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 401–5. https://doi.org/10.5281/zenodo.47559.

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In this work, proposing an image resolution enhancement technique which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different sub bands. Then the three high frequency sub band images have been interpolated using bicubic interpolation. The high frequency sub bands obtained by SWT of the input image are being incremented into the interpolated high frequency sub bands in order to correct the estimated coefficients. In parallel, the input image is also interpolated separately Discrete wavelet transform (DWT) is one of the recent
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Kosugi, Satoshi, and Toshihiko Yamasaki. "Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11296–303. http://dx.doi.org/10.1609/aaai.v34i07.6790.

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This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software such as Adobe® Photoshop® for the following three benefits: enhanced images have no artifacts, the same enhancement can be applied to larger images, and the enhancement is interpretable. To incorporate image editing software into a GAN, we pro
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Dissertations / Theses on the topic "Image enhancement"

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Karelid, Mikael. "Image Enhancement over a Sequence of Images." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12523.

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<p>This Master Thesis has been conducted at the National Laboratory of Forensic Science (SKL) in Linköping. When images that are to be analyzed at SKL, presenting an interesting object, are of bad quality there may be a need to enhance them. If several images with the object are available, the total amount of information can be used in order to estimate one single enhanced image. A program to do this has been developed by studying methods for image registration and high resolution image estimation. Tests of important parts of the procedure have been conducted. The final results are satisfying
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Richardson, Richard Thomas. "Image Enhancement of Cancerous Tissue in Mammography Images." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/39.

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This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined
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Ozyurek, Serkan. "Image Dynamic Range Enhancement." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613603/index.pdf.

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In this thesis, image dynamic range enhancement methods are studied in order to solve the problem of representing high dynamic range scenes with low dynamic range images. For this purpose, two main image dynamic range enhancement methods, which are high dynamic range imaging and exposure fusion, are studied. More detailed analysis of exposure fusion algorithms are carried out because the whole enhancement process in the exposure fusion is performed in low dynamic range, and they do not need any prior information about input images. In order to evaluate the performances of exposure fusion algor
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Chana, Deeph S. "Image restoration exploiting statistical models of the image capture process." Thesis, King's College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246886.

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Tummala, Sai Virali, and Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.

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Emiroglu, Ibrahim. "Fingerprint image enhancement & recognition." Thesis, University of Hertfordshire, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363500.

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Roelofs, Antonius Arnoldus Jozef. "Image enhancement for low vision /." Online version, 1997. http://bibpurl.oclc.org/web/25504.

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Majtanovic, Cveta. "AUTOMATIC ENHANCEMENT OF IMAGE MEMORABILITY." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320352.

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Every picture tells a story. Images are one of the most dominant types of media, uploaded an average in billions every single day and in hundreds of billions on an annual basis. Artifacts depicting visual perception like photographs and other two-dimensional pictures are distributed through the growing number of image-sharing websites. Consequently, a thriving interest in understanding the whole image or objects depicted in it, its style or the emotions a picture might evoke, together with all the other image properties, became increasingly represented in research practice. This research focus
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Bengtsson, Martin, and Emil Ågren. "Image enhancement of license plates in images using Super Resolution." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121682.

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Bildgruppen på enheten för dokument och informationsteknik hos SKL har ett behov av att kunna förbättra bilder med extremt låg upplösning. Detta bildmaterial kan komma från diverse övervakningskameror där det intressanta objektet endast utgör en väldigt liten del, i detta fall registreringsskyltar på förbipasserande bilar. Att skapa en högupplöst bild av en registreringsskylt utav ett fåtal lågupplösta bilder är ett välkänt problem med ett flertal förslag på metoder och lösningar. I denna rapport kommer vi att undersöka vilka metoder som passar bäst vid bildförbättring av registreringsskyltar.
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Adolfsson, Karin. "Visual Evaluation of 3D Image Enhancement." Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7944.

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<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an ill
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Books on the topic "Image enhancement"

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Celebi, Emre, Michela Lecca, and Bogdan Smolka, eds. Color Image and Video Enhancement. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09363-5.

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Wilson, William J. Millimeter-wave sensor image enhancement. National Aeronautics and Space Administration, 1988.

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Arad, Nur. Enhancement by image-dependent warping. Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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J, Wilson William. Millimeter-wave sensor image enhancement. National Aeronautics and Space Administration, 1988.

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Roelofs, T. Image enhancement for low vision. Eindhoven University, 1997.

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Cheung, Kwok-Yin. Signal processing for sonar image enhancement. University of East Anglia, 1993.

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Newell, J. C. W. Archival retrieval: Techniques for image enhancement. British Broadcasting Corporation Research and Development Department, 1995.

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Highnam, Ralph. Mammographic image analysis. Kluwer Academic Publishers, 1999.

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K, Park Stephen, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Digital enhancement of flow field images. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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Kefayati, Sarah. Confocal and two-photon microscopy: Image enhancement. Brock University, Dept. of Physics, 2008.

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Book chapters on the topic "Image enhancement"

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Bauer, Jan, Andrej Sycev, and Karlheinz Blankenbach. "Image Enhancement." In Handbook of Visual Display Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_198.

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Bauer, Jan, Andrej Sycev, and Karlheinz Blankenbach. "Image Enhancement." In Handbook of Visual Display Technology. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35947-7_198-1.

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Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis. Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_4.

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Vyas, Aparna, Soohwan Yu, and Joonki Paik. "Image Enhancement." In Signals and Communication Technology. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7272-7_6.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Enhancement." In Applications of Pulse-Coupled Neural Networks. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_5.

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Highnam, Ralph, and Michael Brady. "Image Enhancement." In Computational Imaging and Vision. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4613-5_7.

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Toennies, Klaus D. "Image Enhancement." In Guide to Medical Image Analysis. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_4.

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Umbaugh, Scott E. "Image Enhancement." In Digital Image Processing and Analysis, 4th ed. CRC Press, 2022. http://dx.doi.org/10.1201/9781003221142-6.

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Chityala, Ravishankar, and Sridevi Pudipeddi. "Image Enhancement." In Image Processing and Acquisition using Python. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429243370-5.

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Tavakoli, Vahid. "Image Enhancement." In CT Scanning of Carbonate Reservoirs. CRC Press, 2023. http://dx.doi.org/10.1201/9781003405412-4.

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Conference papers on the topic "Image enhancement"

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Cottis, Robert A., and Stuart B. Lyon. "Modeling of Image Formation and Image Enhancement for Scanning Electrochemical Methods." In CORROSION 2005. NACE International, 2005. https://doi.org/10.5006/c2005-05056.

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Abstract This paper examines the way in which the image is formed during a SRET measurement, and considers some of the potential sources of error in the measurement. Methods for the enhancement of images to increase the resolution are discussed, and the impact of the various error types on image enhancement processes is considered. It is shown that ideal images can be significantly improved by the conventional image method of Fourier Transform Deconvolution, but that typical errors present in the measurement are liable to seriously limit the capacity for enhancement.
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Soomro, Toufique Ahmed, Mahaveer Rathi, Shafique Ahmed Soomro, Muhammad Usman Keerio, Pardeep Kumar, and Enrique Nava Baro. "Image Enhancement Technique for MRI Brain Images." In 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024. https://doi.org/10.1109/gcwot63882.2024.10805684.

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Zelensky, Alexander, Nikolay V. Gapon, Zhdanova M. Marina, Viacheslav V. Voronin, Yurii Ilukhin, and Andrei Gribkov. "Image enhancement via multiscale-exposure image fusion." In Optoelectronic Imaging and Multimedia Technology XI, edited by Zhenrong Zheng and Jinli Suo. SPIE, 2024. http://dx.doi.org/10.1117/12.3038616.

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"Image enhancement." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586836.

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Liang, Yanmei, Li Yang, and Hailun Fan. "Image enhancement for liver CT images." In International Conference on Optical Instrumentation and Technology, edited by Toru Yoshizawa, Ping Wei, and Jesse Zheng. SPIE, 2009. http://dx.doi.org/10.1117/12.837468.

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Betz, Volkmar, and Joerg-Uwe Meyer. "Image enhancement of microscopic fluorescence images." In Barcelona - DL tentative, edited by Hans-Jochen Foth, Renato Marchesini, Halina Podbielska, Michel Robert-Nicoud, and Herbert Schneckenburger. SPIE, 1996. http://dx.doi.org/10.1117/12.230021.

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Jang, Ben K., and Roger S. Gaborski. "Image enhancement for computed radiographic images." In Medical Imaging 1995, edited by Murray H. Loew. SPIE, 1995. http://dx.doi.org/10.1117/12.208699.

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Aghagolzadeh, Sabzali, and Okan K. Ersoy. "Transform image enhancement." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ml3.

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Blockwise transform image enhancement techniques are discussed. Previously, transform image enhancement has usually been based on the discrete Fourier transform (DFT) applied to the whole image. Two major drawbacks with the DFT are high complexity of implementation involving complex multiplications and additions, with intermediate results being complex numbers, and the creation of severe block effects if image enhancement is done blockwise. In addition, the quality of enhancement is not very satisfactory. In this paper, it is shown that the best transforms for transform image coding, namely, t
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Bhattacharya, Saumik, Sumana Gupta, and Venkatesh K. Subramanian. "Localized image enhancement." In 2014 Twentieth National Conference on Communications (NCC). IEEE, 2014. http://dx.doi.org/10.1109/ncc.2014.6811269.

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Chen, Kuei-Chun. "Color image enhancement." In the 30th annual Southeast regional conference. ACM Press, 1992. http://dx.doi.org/10.1145/503720.503779.

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Reports on the topic "Image enhancement"

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Robinson, J. E. Deconvolution filters and image enhancement. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128047.

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Coffey, Mark. Image Enhancement in a Quantum Environment. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada469994.

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Moore, D. Image enhancement equipment capabilities. Status report. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/5496387.

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Vogel, Curtis R. Computational Methods for Image Reconstruction and Enhancement. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada381745.

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Rupert, J. D., R. A. F. Grieve, J. F. Halpenny, and V. L. Sharpton. Image enhancement system for the ORCATECH graphics computer. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1987. http://dx.doi.org/10.4095/315283.

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Wilson, Gregory L., Andrew C. Lindgren, Thomas M. Fitzgerald, Pamela S. Smith, and Russell C. Hardie. Maximum a Posteriori (MAP) Estimates for Hyperspectral Image Enhancement. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada429581.

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Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Image Modeling and Enhancement via Structured Sparse Model Selection. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada513259.

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Shin, Jun Seob. Novel techniques for image quality enhancement in ultrasound imaging tomography. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215813.

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Felton, Melvin, and Kristan Gurton. Enhancement of Target Contrast in Polarimetric Imagery Using Image Fusion. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada519582.

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Sadler, Laurel C. U.S. Army Research Laboratory Image Enhancement Test Bed User's Manual. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587403.

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