Relevant bibliographies by topics / Image Enhance

Contents

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

Academic literature on the topic 'Image Enhance'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Image Enhance"

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Qu, Zhi, Yaqiong Xing, and Yafei Song. "An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement." Information 9, no. 12 (2018): 308. http://dx.doi.org/10.3390/info9120308.

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Based on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhan
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LIN, YUE-DER, HEN-WEI TSAO, and FOK-CHING CHONG. "AN IMAGE PROCESSING ARCHITECTURE TO ENHANCE IMAGE CONTRAST." Biomedical Engineering: Applications, Basis and Communications 14, no. 05 (2002): 215–17. http://dx.doi.org/10.4015/s1016237202000310.

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To have a good image contrast is an important issue in medical images. This paper introduces a feedback-type image processing architecture that can enhance image contrast without further digital image processing technique, e.g. histogram equalization. Compared with the conventional open-loop imaging system, the images derived by the proposed method has a full-range histogram without causing image distortion, and this is difficult to attain for open-loop imaging system.
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Caspers, Max. "Image Recognition to Enhance the Value of Collections." Biodiversity Information Science and Standards 2 (June 13, 2018): e26320. http://dx.doi.org/10.3897/biss.2.26320.

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Techniques for image recognition through machine learning have advanced rapidly over recent years and applications using this technique are becoming increasingly common.. Applications using image recognition have enormous potential not only for research, education, conservation and capacity-building but certainly also for collections management. Perhaps by now an even bigger challenge than the technological one is supplying content in the form of large amounts of validated images. With an estimated 44 million objects, the collection of Naturalis Biodiversity Center has plenty of physical sourc
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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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Jasper, J., S. Berlin Shaheema, and S. Berlin Shiny. "Natural Image Enhancement Using a Biogeography Based Optimization Enhanced with Blended Migration Operator." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/232796.

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This paper addresses a novel and efficient algorithm for solving optimization problem in image processing applications. Image enhancement (IE) is one of the complex optimization problems in image processing. The main goal of this paper is to enhance color images such that the eminence of the image is more suitable than the original image from the perceptual viewpoint of human. Traditional methods require prior knowledge of the image to be enhanced, whereas the aim of the proposed biogeography based optimization (BBO) enhanced with blended migration operator (BMO) algorithm is to maximize the o
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Ronai, Ze’ev. "Mice enhance their image." Pigment Cell & Melanoma Research 24, no. 2 (2011): 259. http://dx.doi.org/10.1111/j.1755-148x.2011.00840.x.

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Pandurangan, Durai, R. Saravana Kumar, Lukas Gebremariam, L. Arulmurugan, and S. Tamilselvan. "Combined Gray Level Transformation Technique for Low Light Color Image Enhancement." Journal of Computational and Theoretical Nanoscience 18, no. 4 (2021): 1221–26. http://dx.doi.org/10.1166/jctn.2021.9392.

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Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model. Finally, the enh
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Sun, Xiankun, Huijie Liu, Shiqian Wu, Zhijun Fang, Chengfan Li, and Jingyuan Yin. "Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain." International Journal of Digital Multimedia Broadcasting 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9029315.

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We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and refl
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Li, Zhenxuan, Wenzhong Shi, Yongchao Zhu, Hua Zhang, Ming Hao, and Liping Cai. "Subpixel Change Detection Based on Radial Basis Function with Abundance Image Difference Measure for Remote Sensing Images." Remote Sensing 13, no. 5 (2021): 868. http://dx.doi.org/10.3390/rs13050868.

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Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image difference measure (AIDM) is designed and utilized to enhance the subpixel mapping (SPM) by borrowing the fine spatial distribution of the fine spatial resolution image to decrease the influence of
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AbuNaser, Amal, Iyad Abu Doush, Nahed Mansour, and Sawsan Alshattnawi. "Underwater Image Enhancement Using Particle Swarm Optimization." Journal of Intelligent Systems 24, no. 1 (2015): 99–115. http://dx.doi.org/10.1515/jisys-2014-0012.

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AbstractThis article introduces a framework for enhancing underwater images using the particle swarm optimization algorithm. A pre-processing step is introduced to reduce the absorbing and scattering effects of water before applying a filter based on this algorithm to enhance the image. The quality of enhanced images is quantitatively assessed by applying the framework on a dataset of underwater images. The obtained results show a considerable improvement.
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Dissertations / Theses on the topic "Image Enhance"

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Mosbruger, Michael C. "Alternative audio solution to enhance immersions in deployable synthetic environments." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FMosbruger.pdf.

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Thesis (M.S. in Modeling, Virtual Environments, and Simulation)--Naval Postgraduate School, September 2003.<br>Thesis advisor(s): Russell D. Shilling, Rudolph P. Darken. Includes bibliographical references (p. 169-172). Also available online.
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Al-atia, Mohassad. "Can oral contrast enhance image quality at MRCP? A literature review." Thesis, Örebro universitet, Institutionen för hälsovetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-50927.

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Forsgren, Edvin. "Deep Learning to Enhance Fluorescent Signals in Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-175328.

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Fife, Megan Melissa. "A Meaning-Based Instruction to Enhance Literacy Learning in a Dual-Language Kindergarten Classroom." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1192.pdf.

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Chen, Lan. "Polymerization of monomers within hoop pine to enhance harness for appearance grade applications /." [St. Lucia, Qld], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18196.pdf.

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Whitezell, Marc A. "Development of a Strain Energy Storage Mechanism Using Tension Elements to Enhance Golf Club Performance." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1258.pdf.

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Pheng, Sophea. "The potential for exploiting allelopathy to enhance weed management in Cambodian rice farming systems /." [St. Lucia, Qld], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18286.pdf.

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Mohammed, Salifu Dauda. "THE IMPACT OF RECRUITMENT SOURCES ON BRAND IMAGE PERCEPTIONS AND ORGANIZATIONAL ATTRACTION: LEVERAGING ORGANIZATIONAL BRAND IMAGE PERCEPTIONS TO ENHANCE RECRUITMENT ATTRACTION." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/567577.

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Business Administration/Human Resource Management<br>D.B.A.<br>In today’s competitive labor markets, successfully recruiting a large pool of skilled and qualified job applicants is a prime concern of many organizations. In Study 1, I focused on how organizations can successfully employ four traditional recruitment practices (sponsorships, job fair activities, word-of mouth endorsements and corporate advertisements) simultaneously to disseminate information about their positive recruitment brand images to job seekers to enhance organizational attraction. The results which supported all my hypot
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Szmigielska, Emilia. "Our bodies : a mixed methods study of an internet-based body image intervention using feminist theory to enhance positive body image." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/21140/.

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Aims: The aim of the current study is to investigate the usefulness of an internet based positive body image intervention for women which incorporates feminist ideas and media literacy. This novel study will be an initial trial with a non-clinical population of women looking to learn about body image in order to evaluate if it is feasible as an intervention to improve body image in this format. Methods: The present study employed a sequential mixed methods prepost within groups online intervention outcomes study design, whereby quantitative and qualitative data collection and analysis were seq
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McLaughlin, Peter Shepherd 1955, and Peter Shepherd 1955 McLaughlin. "Applications of digital terrain data to enhance the utility of remotely sensed multispectral image data." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/191958.

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This research demonstrates two different applications of digital terrain data for improving multispectral classification of forest and range cover types. One method involves a stratification process to determine probabilities of occurrence and to develop an ecological distribution model for the six major vegetation types within the eastern San Francisco Volcanic Field in Arizona. The second method employs reflectance modeling techniques to reduce brightness variations resulting from topography and illumination in Landsat image data of the area in order to examine the intrinsic qualities of the
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Books on the topic "Image Enhance"

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First impressions: Tips to enhance your image. Addicus Books, 1996.

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Image art workshop: Creative ways to embellish and enhance photographic images. CPi Lifestyles, 2009.

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Parente, Diane. Mastering your professional image: Dressing to enhance your credibility. Image Development and Management, 1995.

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Crowther, Frank. The image of the teaching profession: Queensland teachers discuss ways to enhance the image of their profession. Queensland Institute for Educational Administration, 1991.

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Knight, John. Trust and country image: Perceptions of European food distributors regarding factors that could enhance or damage New Zealand's image - including GMOs. University of Otago, 2003.

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Publicity for mental health clinicians: Using TV, radio, and print media to enhance your public image. Haworth Press, 1995.

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Kendrick, Susan. Elizabeth I's use of virginity to enhance her sovereignty: Managing the image of a sixteenth-century queen. Edwin Mellen Press, 2009.

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Elizabeth I's use of virginity to enhance her sovereignty: Managing the image of a sixteenth-century queen. Edwin Mellen Press, 2009.

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Kendrick, Susan. Elizabeth I's use of virginity to enhance her sovereignty: Managing the image of a sixteenth-century queen. Edwin Mellen Press, 2009.

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Duda, Doug. Guide to sponsored video: Using home video to sell your product, enhance your image, and create new profit sources. Knowledge Industry Publications, 1987.

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Book chapters on the topic "Image Enhance"

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Musolino, Marialisa, Mariangela Maesano, and Giuliana Bilotta. "Submerged Photogrammetric Survey: A Methodology to Enhance Image." In New Metropolitan Perspectives. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48279-4_149.

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Dasiopoulou, Stamatia, Ioannis Kompatsiaris, and Michael G. Strintzis. "Using Fuzzy DLs to Enhance Semantic Image Analysis." In Semantic Multimedia. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92235-3_5.

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Shima, Hisashi. "Common Understanding of Graphic Image Enhance "Emotional Design"." In Human Centered Design. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02806-9_63.

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Soedirdjo, Subaryani D. H., Ine Renata Musa, Tati L. R. Mengko, and Iwan Sovani. "Low Vision Aid with Image to Text Converter to Enhance Magnified Text Image." In IFMBE Proceedings. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14515-5_353.

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Hlavka, Christine A., and Jennifer L. Dungan. "Application of Geostatistical Simulation to Enhance Satellite Image Products." In Geostatistics Banff 2004. Springer Netherlands, 2005. http://dx.doi.org/10.1007/978-1-4020-3610-1_95.

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Samuel, Tsion, Dawit Assefa, and Ondrej Krejcar. "Framework for Effective Image Processing to Enhance Tuberculosis Diagnosis." In Intelligent Information and Database Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75420-8_36.

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Llorente, Ainhoa, and Stefan Rüger. "Using Second Order Statistics to Enhance Automated Image Annotation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00958-7_52.

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Turner, S. J., P. D. Picton, and J. A. Campbell. "Selecting Potentials Filter Banks to Enhance Evoked Recordings Using Evolutionary Algorithms." In Evolutionary Image Analysis, Signal Processing and Telecommunications. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10704703_8.

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Irfan, Muhammad, Jiangbin Zheng, Muhammad Iqbal, and Muhammad Hassan Arif. "A Novel Feature Extraction Model to Enhance Underwater Image Classification." In Intelligent Computing Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43364-2_8.

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Fadhel, Mohammed A., and Omran Al-Shamma. "Implementing a Hardware Accelerator to Enhance the Recognition Performance of the Fruit Mature." In Recent Trends in Signal and Image Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6966-5_5.

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Conference papers on the topic "Image Enhance"

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Sun, Xiaopeng, Muxingzi Li, Tianyu He, and Lubin Fan. "Enhance Image as You Like with Unpaired Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/140.

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Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images f
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Zhang, Ran, Junchi Bin, Zheng Liu, and Erik Blasch. "Image translation to enhance IR2VIS image registration." In Geospatial Informatics XI, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, and Joshua D. Harguess. SPIE, 2021. http://dx.doi.org/10.1117/12.2588034.

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T, Ashwin, Rahul Gupta, and Sugata Ghosal. "Leveraging non-relevant images to enhance image retrieval performance." In the tenth ACM international conference. ACM Press, 2002. http://dx.doi.org/10.1145/641007.641077.

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Wu, Fangyu, Weihang You, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang. "Image-Image Translation to Enhance Near Infrared Face Recognition." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8804414.

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Malcolm, J. "Using escrowed public keys to enhance confidence in an image authentication scheme." In IEE Seminar on Secure Images and Image Authentication. IEE, 2000. http://dx.doi.org/10.1049/ic:20000222.

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Messias, Lucas R. V., Cristiano R. Steffens, Paulo L. J. Drews-Jr, and Silvia S. C. Botelho. "UCAN: A Learning-based Model to Enhance Poorly Exposed Images." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.13004.

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Image enhancement is a critical process in imagebased systems. In these systems, image quality is a crucial factor to achieve a good performance. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images, and, with that, we can have the underexposure and/or overexposure problem. In this work, our aim is to restore illexposed images. For this purpose, we present UCAN, a small and fast learning-based model capable to restore and enhance poorly exposed images. The obtained results are evaluated using ima
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Seifert, M., and V. Ramesh. "How to enhance security by image analytics." In IET International Conference on Railway Engineering 2008 (ICRE 2008). IEE, 2008. http://dx.doi.org/10.1049/ic:20080008.

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Tazi, S. N., and Vinesh Kr Jain. "Enhance matching in multi-dimensional image reconstruction using stereo image sequences." In 2014 International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2014. http://dx.doi.org/10.1109/iciscon.2014.6965214.

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Furtado, Pedro. "Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2511986.

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Benitez-Restrepo, Hernan D., Alan C. Bovik, and Camilo G. Rodriguez Pulecio. "Image quality assessment to enhance infrared face recognition." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296392.

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Reports on the topic "Image Enhance"

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Cui, Yonggang. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1477475.

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Cui, Yonggang, and Maikael A. Thomas. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1436246.

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Cui, Y. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1413952.

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Cui, Yonggang. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1524538.

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Sarofim, Samer. Developing an Effective Targeted Mobile Application to Enhance Transportation Safety and Use of Active Transportation Modes in Fresno County: The Role of Application Design & Content. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2013.

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This research empirically investigates the need for, and the effective design and content of, a proposed mobile application that is targeted at pedestrians and cyclists in Fresno County. The differential effect of the proposed mobile app name and colors on the target audience opinions was examined. Further, app content and features were evaluated for importance and the likelihood of use. This included design appeal, attractiveness, relevance, ease of navigation, usefulness of functions, personalization and customization, message recipients’ attitudes towards message framing, and intended behav
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Choung, George P. A Shape Preserving and Noise Removing Image Enhancer through Regularization. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387116.

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McIntosh, Robert E. Use of Microwave Polarimetry to Enhance SAR Images of the Ocean Surface. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273568.

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Weidemann, Alan D., G. R. Fournier, J. L. Forand, P. Mathieu, and Scott McLean. Using a Laser Underwater Camera Image Enhancer for Mine Warfare Applications: What is Gained? Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada417137.

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Khalsa, Kimberly A., and Jeffery A. Fessler. Regularized Reconstruction of Dynamic Contrast-Enhanced MR Images for Evaluation of Breast Lesions. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535361.

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Khalsa, Kimberly A. Regularized Reconstruction of Dynamic Contrast-Enhanced MR Images for Evaluation of Breast Lesions. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada542286.

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