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: 22 June 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 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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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 (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 not effective. For more effective noise reduction, the digital image is transferred from the RGB color model to the HSV or LAB color model, where the brightness and color components of the digital noise can be filtered separately. Color noise is suppressed in the color channels of the image using a Gaussian filter. Noise reduction in the brightness channel of a digital image is more difficult task, especially at the edge of sharp transitions of brightness. To suppress the brightness noise in contrast images, it is proposed to use a nonlinear filter based on the generalized method of least absolute values (GMLAV). The process of smoothing the contrast noisy transition by the GMLAV-filter is described, and its efficiency is shown in comparison with the median filtration.
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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 Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.
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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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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 paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression.
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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 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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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 digital form. This goal requires the development of new geophysical remote sensing technologies to create underground images down to the depths of most buried utilities in the United States and the development of software to extract features from the images to create digital maps that can be archived electronically - for example, in Geographic Information Systems. Key components of each of these goals were developed and demonstrated during the Dual-Array Project.
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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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