Готові списки джерел за темами / Digital signal and image processing

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Добірка наукової літератури з теми "Digital signal and image processing"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Digital signal and image processing"

Braccini, C. "Digital image signal processing." Signal Processing 17, no. 2 (June 1989): 185–86. http://dx.doi.org/10.1016/0165-1684(89)90023-6.

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Bingol, A. "Digital image processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 4 (August 1985): 1063–64. http://dx.doi.org/10.1109/tassp.1985.1164618.

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Chen, Qunying. "Stepped Frequency Multiresolution Digital Signal Processing." Scientific Programming 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9081988.

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With the rapid development of radar industry technology, the corresponding signal processing technology becomes more and more complex. For the radar with short-range detection function, its corresponding signal mostly presents the characteristics of wide bandwidth and multiresolution. In the traditional data processing process, a large number of signals will interfere with the signal, which makes the final signal processing difficult or even impossible. Based on this problem, this paper proposes a principal component linear prediction processing algorithm based on clutter suppression processing on the basis of traditional signal processing algorithm. According to the curve characteristics of the data returned by the target detected by the signal, through certain image signal measurement and transformation, the clutter can be effectively suppressed and the typical characteristics of the corresponding target curve can be enhanced. For the convergence problem of signal processing and the corresponding image chromatic aberration compensation problem, this paper will realize the chromatic aberration compensation of the corresponding target echo image based on the radial pointing transverse mode algorithm and enhance the convergence speed of the whole algorithm system. In the experimental part of this paper, the optimization algorithm proposed in this paper is compared with the traditional algorithm. The experimental results show that the algorithm proposed in this paper has obvious advantages in the convergence of signal processing and antijamming performance and has the promotion value.

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Silverman, Jason, Gail L. Rosen, and Steve Essinger. "Applications in Digital Image Processing." Mathematics Teacher 107, no. 1 (August 2013): 46–53. http://dx.doi.org/10.5951/mathteacher.107.1.0046.

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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.

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Yamamoto, Yutaka, Kaoru Yamamoto, Masaaki Nagahara, and Pramod P. Khargonekar. "Signal processing via sampled-data control theory." Impact 2020, no. 2 (April 15, 2020): 6–8. http://dx.doi.org/10.21820/23987073.2020.2.6.

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Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.

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Skorton, David J., Steve M. Collins, Ernest Garcia, Edward A. Geiser, Wayne Hillard, William Koppes, David Linker, and Gary Schwartz. "Digital signal and image processing in echocardiography." American Heart Journal 110, no. 6 (December 1985): 1266–83. http://dx.doi.org/10.1016/0002-8703(85)90024-9.

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Osten, Evariste F., and John C. Schultz. "A system for fast digital image processing of asynchronous SEM signals." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 676–77. http://dx.doi.org/10.1017/s0424820100105448.

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The time required to examine a specimen's features with an SEM before photographically recording representative images is related to the amount of visual information about that specimen that is available from the SEM's viewing CRT. In a laboratory that examines several thousand specimens each year, many in low signal-to-noise situations, the accumulated examination time can be significant. Image processing to increase the information content of the viewed image can reduce the time needed to examine the specimen. Digital frame integration can be used to improve an image's signal-to-noise ratio and color processing of the observed image can be used to provide enhanced visual perception. Using a passive interface with the SEM for image processing has the advantage that it doesn't interfere with the SEM scan electronics nor does it affect normal SEM operation. A difficulty in image processing arises when using asynchronous SEM signals - video signals that lack synch pulses and therefore do not conform to standard RS-170 video.

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Van de Lest, C. H., E. M. Versteeg, J. H. Veerkamp, and T. H. Van Kuppevelt. "Elimination of autofluorescence in immunofluorescence microscopy with digital image processing." Journal of Histochemistry & Cytochemistry 43, no. 7 (July 1995): 727–30. http://dx.doi.org/10.1177/43.7.7608528.

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Autofluorescence can be a very disturbing factor in immunofluorescence microscopy. We present here a method to eliminate autofluorescence. The method is based on the fact that most autofluorescent compounds have broad-banded excitation and emission spectra, whereas specific fluorescent probes have narrow spectra. Two images are recorded and digitized, one at a wavelength exciting both the fluorescent probe and the autofluorescent molecules, and one at a wavelength exciting only the latter. Subtraction of the autofluorescence signal from the total fluorescence signal, using a self-developed computer program, results in an autofluorescence-free image. The procedure is demonstrated for elimination of elastin-derived autofluorescence in human lung alveoli and for elimination of lipofuscin-derived autofluorescence in human heart muscle. The autofluorescence signal is positively correlated with tissue section thickness (r = 0.93; p < 0.0001), and can be used to correct the specific fluorescence signals for section thickness.

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Sanz, J., and E. Hinkle. "Computing projections of digital images in image processing pipeline architectures." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 2 (February 1987): 198–207. http://dx.doi.org/10.1109/tassp.1987.1165123.

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Дисертації з теми "Digital signal and image processing"

Ahtaiba, Ahmed Mohamed A. "Restoration of AFM images using digital signal and image processing." Thesis, Liverpool John Moores University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604322.

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

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Musoke, David. "Digital image processing with the Motorola 56001 digital signal processor." Scholarly Commons, 1992. https://scholarlycommons.pacific.edu/uop_etds/2236.

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This report describes the design and testing of the Image56 system, an IBM-AT based system which consists of an analog video board and a digital board. The former contains all analog and video support circuitry to perform real-time image processing functions. The latter is responsible for performing non real-time, complex image processing tasks using a Motorola DSP56001 digital signal processor. It is supported by eight image data buffers and 512K words of DSP memory (see Appendix A for schematic diagram).

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Hartley, David Andrew. "Image correlation using digital signal processors." Thesis, Liverpool John Moores University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304465.

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Zhu, Yong. "Digital signal and image processing techniques for ultrasonic nondestructive evaluation." Thesis, City University London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336431.

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May, Heather. "Wavelet-based Image Processing." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037498.

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Ansourian, Megeurditch N. "Digital signal processing for the analysis of fetal breathing movements." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/13595.

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Silva, Eduardo Antonio Barros da. "Wavelet transforms for image coding." Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282495.

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Lu, Nan. "Development of new digital signal processing procedures and applications to speech, electromyography and image processing." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445962.

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Lie, Chin Cheong Patrick. "Iterative algorithms for fast, signal-to-noise ratio insensitive image restoration." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63767.

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Shakaff, A. Y. Md. "Practical implementation of the Fermat Number Transform with applications to filtering and image processing." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379766.

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Книги з теми "Digital signal and image processing"

Digital image signal processing. Boston: Artech House, 1987.

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Digital image processing. New York: Prentice Hall, 1993.

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Digital signal and image processing. Hoboken, NJ: J. Wiley, 2004.

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P, Banks Stephen. Signal processing, image processing, and pattern recognition. New York: Prentice Hall, 1990.

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Two-dimensional signal and image processing. London: Prentice-Hall International, 1990.

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Two-dimensional signal and image processing. Englewood Cliffs, N.J: Prentice Hall, 1990.

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Rajan, E. G. Symbolic computing: Signal and image processing. India: B.S. Publications, 2005.

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Wang, Bu-Chin. Digital signal processing techniques and applications in radar image processing. Hoboken, N.J: John Wiley, 2008.

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Wang, Bu-Chin. Digital signal processing techniques and applications in radar image processing. Hoboken, N.J: John Wiley, 2008.

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Multidimensional signal, image, and video processing and coding. 2nd ed. Amsterdam: Academic Press, 2012.

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Частини книг з теми "Digital signal and image processing"

Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Filtering." In Digital Signal Processing, 51–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_2.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Adaptive Image Filters." In Digital Signal Processing, 107–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_3.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Segmentation." In Digital Signal Processing, 237–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_6.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Compression." In Digital Signal Processing, 279–328. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_7.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Companion Image Processing Software." In Digital Signal Processing, 349–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_9.

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Gan, Woon Siong. "Digital Signal Processing." In Signal Processing and Image Processing for Acoustical Imaging, 41–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-5550-8_9.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Enhancement and Restoration." In Digital Signal Processing, 209–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_5.

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Gan, Woon Siong. "Digital Image Processing." In Signal Processing and Image Processing for Acoustical Imaging, 47–51. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-5550-8_10.

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Zieliński, Tomasz P. "Image Processing." In Starting Digital Signal Processing in Telecommunication Engineering, 439–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49256-4_16.

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Pitas, I., and A. N. Venetsanopoulos. "Morphological Image and Signal Processing." In Nonlinear Digital Filters, 151–215. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-6017-0_6.

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Тези доповідей конференцій з теми "Digital signal and image processing"

"Session: Digital image and signal processing." In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2011. http://dx.doi.org/10.1109/idaacs.2011.6072782.

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"Digital Signal and Image Processing III." In 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). IEEE, 2021. http://dx.doi.org/10.1109/icest52640.2021.9483493.

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Kshirsagar, Shirish P., David A. Hartley, David M. Harvey, and Clifford A. Hobson. "Parallel digital signal processing architectures for image processing." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Franklin T. Luk. SPIE, 1994. http://dx.doi.org/10.1117/12.190876.

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Harvey, D. M. "Digital signal processing systems architectures for image processing." In Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950701.

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Kyprianou, Ross, Peter Schachte, and Bill Moran. "Dauphin: A Signal Processing Language - Statistical Signal Processing Made Easy." In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2015. http://dx.doi.org/10.1109/dicta.2015.7371250.

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Sundaram, R. "Discrete Filters And Transforms To Localize Signal Transitions." In Digital Image Processing and Analysis. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/dipa.2010.dmc5.

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Hartenstein, Reiner W., A. G. Hirschbiel, K. Lemmert, M. Riedmueller, Karin Schmidt, and M. Weber. "Xputer use in image processing and digital signal processing." In Lausanne - DL tentative, edited by Murat Kunt. SPIE, 1990. http://dx.doi.org/10.1117/12.24266.

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Qu, Dongdong, Jiuling Jia, and Jian Zhou. "Digital alias-free signal processing methodology for sparse multiband signals." In 2013 6th International Congress on Image and Signal Processing (CISP). IEEE, 2013. http://dx.doi.org/10.1109/cisp.2013.6743865.

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Bateman, Philip, Anthony T. S. Ho, and Alan Woodward. "Image forensics of digital cameras by analysing image variations using Statistical Process Control." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397649.

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Pourebrahimi, B., J. C. A. van der Lubbe, and G. Dietz. "Digital Analysis of Papers for the Authentication and Dating of Art." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.710-056.

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Звіти організацій з теми "Digital signal and image processing"

Author, Not Given. Real-Time Digital Signal Processing for a Fourier Transform Hyperspectral Imager. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/8363.

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Thomas, J. B., and K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada203744.

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Roberts, Richard A. VLSI Implementations for Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada189612.

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Cathey, W. T., E. R. Dowski, Sara Bradburn, and Greg Johnson. Matched Image Formation/Digital Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada328217.

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Benedetto, John J. New Techniques in Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada380026.

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Karalamangala, Arun S. Signal Processing for High-Resolution Image Formation. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada290648.

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Wahl, Daniel E., and David A. Yocky. Bistatic SAR: Signal Processing and Image Formation. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1159449.

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Cohen, Leon. Signal and Image Processing in Different Representations. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada477452.

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Chung, Y., L. Emery, and J. Kirchman. Digital signal processing for beam position feedback. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/90669.

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Willson, Jr, and Alan N. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada277617.

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