Relevant bibliographies by topics / Digital signal and image processing

Contents

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

Academic literature on the topic 'Digital signal and image processing'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Digital signal and image processing"

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Braccini, C. "Digital image signal processing." Signal Processing 17, no. 2 (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 (1985): 1063–64. http://dx.doi.org/10.1109/tassp.1985.1164618.

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

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Surin, V. A. "ON PROCESSING NOISY CONTRAST IMAGES." Bulletin of the South Ural State University series "Mathematics. Mechanics. Physics" 13, no. 1 (2021): 14–21. http://dx.doi.org/10.14529/mmph210102.

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The problem of noise reduction at sharp transitions of brightness in digital noisy contrast images is considered. In addition to the useful signal, digital images obtained by digitizing an analogue signal with a digital photo matrix have a noise component. Moreover, to obtain a digital image in the standard RGB color model, a demosaicing interpolation algorithm must be applied to the image obtained from a digital photo matrix. Due to such transformations, the Gaussian distribution of noise in a digital noisy image is violated. Using a standard image digitization model for noise reduction is no
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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 (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 Y
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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
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R, Manikandan, Muruganantham Ponnusamy, and Jayasri Subramaniam. "MATHEMATICAL MORPHOLOGY BASED DIGITAL IMAGE ENHANCEMENT PROCESSING WITH CROSS SEPARATE BOUNDARY OBJECTS." ICTACT Journal on Image and Video Processing 12, no. 4 (2022): 2699–703. https://doi.org/10.21917/ijivp.2022.0383.

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In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this p
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Skorton, David J., Steve M. Collins, Ernest Garcia, et al. "Digital signal and image processing in echocardiography." American Heart Journal 110, no. 6 (1985): 1266–83. http://dx.doi.org/10.1016/0002-8703(85)90024-9.

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Dissertations / Theses on the topic "Digital signal and image processing"

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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 simulat
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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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Books on the topic "Digital signal and image processing"

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Takao, Nishitani, Ang Peng H, and Catthoor Francky, eds. VLSI video/image signal processing. Kluwer Academic Publishers, 1993.

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

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

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

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Pena, Lopez Fernando, and J. Richard Duro. Digital Image and Signal Processing for Measurement Systems. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337911.

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Najim, Mohamed, ed. Digital Filters Design for Signal and Image Processing. ISTE, 2006. http://dx.doi.org/10.1002/9780470612064.

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Blanchet, Gérard, and Maurice Charbit. Digital Signal and Image Processing using MATLAB®. ISTE, 2006. http://dx.doi.org/10.1002/9780470612385.

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Blanchet, Gérard, and Maurice Charbit. Digital Signal and Image Processing Using Matlab®. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118999554.

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Blanchet, Gérard, and Maurice Charbit. Digital Signal and Image Processing Using Matlab®. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118999592.

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Blanchet, Gérard, and Maurice Charbit. Digital Signal and Image Processing Using MATLAB®. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119054009.

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Book chapters on the topic "Digital signal and image processing"

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Filtering." In Digital Signal Processing. 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. 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. 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. 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. 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. 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. 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. 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. 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. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-6017-0_6.

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Conference papers on the topic "Digital signal and image processing"

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"Signal Processing & Digital Image Processing." In 2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). IEEE, 2022. http://dx.doi.org/10.1109/icest55168.2022.9828659.

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"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. 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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Reports on the topic "Digital signal and image processing"

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

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Author, Unknown. DTRS56-02-T-0005 Digital Mapping of Buried Pipelines with a Dual Array System. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0011943.

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The technical goal of the Dual Array Project was to develop new technology for non-invasive mapping of buried pipelines, down to depths of 10 meters or more, using modern electromagnetic sensors and signal processing. A major proposed innovation in the work was the integration of the sensor arrays and software into a mobile system capable of mapping underground utility networks (and other buried infrastructure) efficiently over large areas. Ultimately, the goal is to have a non-invasive system that can produce an accurate infrastructure map of an entire urban or suburban utility network in dig
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Thomas, J. B., and K. Steiglitz. Digital Signal Processing. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203744.

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Karalamangala, Arun S. Signal Processing for High-Resolution Image Formation. Defense Technical Information Center, 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), 2014. http://dx.doi.org/10.2172/1159449.

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

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

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Roberts, Richard A. VLSI Implementations for Digital Signal Processing. Defense Technical Information Center, 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. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328217.

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DeVore, Ronald A. Advanced Wavelet Methods for Image and Signal Processing. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417316.

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