Готові списки джерел за темами / Digital signal and image processing / Статті в журналах

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

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 19 липня 2025

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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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Pradhan, Manini Monalisa. "Elimination Noise from Image Using Machine Learning Techniques." Oct-Nov 2023, no. 36 (October 20, 2023): 27–36. http://dx.doi.org/10.55529/jipirs.36.27.36.

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The Image Processing system is mostly used because of their easy accessibility of powerful personal computers, bulk memory machines with graphics software and others visual application. Of “Image Processing” is applied in a number of applications. These include in area of Remote Sensing in GIS application, Medical Imaging Processing for patient care application, Forensic Studies, Textiles engineering and design, Material science, Military Research, Film industry application, and Document processing, Graphic arts. An image is defined as an array, or a matrix, square pixel arranged in rows and columns. Many image-processing procedures involve making the image as a two-dimensional signal and applying standard signal processing techniques to it. Image processing can be defined by means of a ‘digital image processing’’. The pitch of ‘digital image processing’ states to ‘processing digital’ images through channels of a computer. In this paper Image de-noising through K-SVD algorithm is presented by taking the RGB color with 256*256 sizes 24 bit standardize image.

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Jiang, Xiaolei. "Fourier Transforms in Digital Image Processing Courses." Journal of Education and Educational Research 9, no. 3 (2024): 190–92. http://dx.doi.org/10.54097/dx94s002.

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To help teaching and learning of Fourier transforms in digital image processing courses, an approach to the Fourier transforms from a standpoint of linear algebra is presented. After representing a periodic sequence by a circulant matrix, the periodic convolution can be formulated in terms of matrix multiplication, and finally, the Fourier transform is written in a form of matrix diagonalization. As a comparison, summation formulas are prevalent in traditional courses on signals and systems and matrices are scarcely found in textbooks of digital signal processing. We hope that this approach will make the Fourier transform easier to teach and learn in digital image processing courses.

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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 (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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Tariq, Mashal, Ayesha A. Siddiqi, Ghous Baksh Narejo, and Shehla Andleeb. "A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (2018): 66–73. http://dx.doi.org/10.2174/1573405613666170614081434.

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Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists. Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images. Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection. Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.

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Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration." Applied and Computational Engineering 17, no. 1 (2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.

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Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.

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Setyowati, Anggriya Feby, Pratiwi Sri Wardani, Erlinda Ratnasari Putri, and Devina Rayzy Perwitasari Sutaji Putri. "Pengolahan Citra Digital EKG Rumah Sakit Tk.IV Samarinda." Progressive Physics Journal 5, no. 1 (2024): 343. http://dx.doi.org/10.30872/ppj.v5i1.1037.

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In the hospital itself, especially in the MCU Poly Radiology Installation, it is a unit that performs services such as Medical Check Up, making health certificates, and carrying out routine checks at an institution. ECG examination is one of the services most frequently performed routinely by patients. Examinations carried out include medical check-ups, monitoring of long-term therapy, patient to assessment before surgery, individual screening for high-risk jobs, examinations before participating in sporting events, and overall examinations as a condition of employment. This study carried out the process of withdrawing information or object descriptions or identifying objects contained in images by compressing or reducing image data. By using EKG data from TK.IV Samarinda Hospital by carrying out processing stages such as converting analog data to digital, cropping, and applying filters to EKG images. After the filter is applied to the image, it is found that after 5 types of image processing (RGB Image, Grayscale Image, Binary Image, Thresholding Image, and Edge Detection Image) on the results of the ECG signal it can be concluded that the processed signal retains the features of the existing signal and information. . Of the five types of processing methods, the clearest result is conversion to Edge Detection imagery.

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Mahdi, Naghibi. "New Research Articles 2019 October Issue Signal & Image Processing An International Journal (SIPIJ)." Signal & Image Processing : An International Journal (SIPIJ) 10, no. 5 (2019): 1–12. https://doi.org/10.5281/zenodo.3524933.

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Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

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Kavitha S Patil,. "Digital Image and Video Processing: Algorithms and Applications." Journal of Electrical Systems 20, no. 3s (2024): 1390–96. http://dx.doi.org/10.52783/jes.1516.

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Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings. When it comes to image and video processing, the input is an image of poor quality, and the output is an image and video of higher quality. Research on algorithms and applications of digital image and video processing is the primary purpose of this study, which aims to investigate these topics extensively. The methodology employed in this study is qualitative research technique. In accordance with the findings of this research, "Image Processing" refers to the process of analyzing images with the objective of determining the significance of objects and identifying them. Image analysts analyze data that has been remotely sensed and attempt to detect, identify, classify, measure, and evaluate the significance of physical and cultural objects, as well as their patterns and spatial relationships. One subcategory of signal processing is known as video processing, and it is distinguished by the fact that the signals that are input and output are video files or video streams. Technology such as television sets, videocassette recorders (VCRs), DVD players, and other devices all make use of video processing algorithms. The processing of images and videos is extremely useful in a variety of contexts.

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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 (1987): 198–207. http://dx.doi.org/10.1109/tassp.1987.1165123.

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Yang, Hong Peng. "Computer Digital Image Processing Application and Research." Advanced Materials Research 926-930 (May 2014): 3389–92. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3389.

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This paper Based on the analysis of the characteristics of image information and image information technology, analyzes some field demand for image information technology, analyzed the image application of news communication management, Analysis of domestic and foreign research present situation, proposed to increase the image signal, research ideas application depth and the breadth of color and its technology in the field , to accelerate the process of social normalization.

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Desai, Sashwat Bilvesh. "Image Processing Techniques: A Review." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 1302–8. https://doi.org/10.5281/zenodo.7691869.

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Following recent trends, the domain of Image Processing is growing vastly. The modern world is becoming more and more digital, and hence the need for Digital Image Processing is essential in order to provide more effective solutions such as an image with better resolution and clarity or an image in compressed form to reduce the space it occupies. Image Processing can be considered synonymous to Signal Processing in the way that both involve techniques to improve a digital entity. This paper includes an overview of the different Image Processing Techniques

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Zhong, Xiaolei. "Design of Digital Image Processing System Based on Machine Vision." Academic Journal of Science and Technology 6, no. 3 (2023): 100–104. http://dx.doi.org/10.54097/ajst.v6i3.10391.

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Digital image processing technology is a technology that has developed rapidly in recent years[1]. A series of images are processed by computers to improve the visual effect of the image in order to achieve the desired presentation effect. Digital image processing technology has been continuously developed with the innovation of computers, and its application range has expanded from the original image field to traffic command, signal processing, medical treatment and other fields closely related to people's lives. With the support of many algorithms, the speed of digital image processing technology is getting faster and faster, which brings a lot of convenience to people's lives, and which becomes an indispensable technology in life. With the help of MATLAB's GUI user interface, image processing toolbox and MATLAB's comprehensive mathematical function library, this article designs an image processing operating platform that realizes some image processing functions after repeated trials.

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Basha, Shaik Mahaboob, and B. C. Jinaga. "A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images." ISRN Signal Processing 2013 (August 7, 2013): 1–5. http://dx.doi.org/10.1155/2013/539759.

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The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on Golomb-Rice coding which was efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of peak signal-to-noise ratios (PSNR). We considered medical images, satellite extracted images, and natural images for the inspection and proposed a novel technique to increase the visual quality of the reconstructed image.

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He, Yang Ming, Yan Qiu He, Guang Yao Xiong, and Ming Feng Zhu. "Application of Halcon in Digital Image and Signal." Applied Mechanics and Materials 716-717 (December 2014): 1338–40. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1338.

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Halcon is a machine vision software. It can process all kinds of image processing problem. In this paper, Halcon is used to process moving targets detection, from which it can be seen how the software Halcon is used to process digital image. The algorithm includes region_ growing algorithm, image filtering and morphological operators, etc. The software Halcon can be used in digital signal, too. In the end, it is used to process pulse signal. The result shows that Halcon has powerful functions in digital data processing.

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Mohanty, Sumant Sekhar, and Sushreeta Tripathy. "Application of Different Filtering Techniques in Digital Image Processing." Journal of Physics: Conference Series 2062, no. 1 (2021): 012007. http://dx.doi.org/10.1088/1742-6596/2062/1/012007.

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Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.

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Gehlbach, Steve M., and F. Graham Sommer. "Frequency Diversity Speckle Processing." Ultrasonic Imaging 9, no. 2 (1987): 92–105. http://dx.doi.org/10.1177/016173468700900202.

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Ultrasonic waveform data from a tissue-mimicking phantom containing low contrast targets was digitized, stored and processed prior to creating and displaying ultrasonic images. Speckle reduction was performed by digital filtering of the waveform data with appropriately spaced and weighted digital filters, prior to both coherent and incoherent image averaging. The resultant images showed increased signal-to-noise ratios, consistent with theory. Better definition of low contrast target boundaries was noted in the processed, compared to unprocessed, images. Incoherent and coherent processing were investigated, and appeared to be equivalent.

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Uomori, K., A. Morimura, H. Ishii, T. Sakaguchi, and Y. Kitamura. "Automatic image stabilizing system by full-digital signal processing." IEEE Transactions on Consumer Electronics 36, no. 3 (1990): 510–19. http://dx.doi.org/10.1109/30.103167.

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Su, Su Yi Mon, and Jian Cheng Fang. "Analysis of Synthetic Aperture Radar Imaging and Signal Processing." Advanced Materials Research 433-440 (January 2012): 2004–10. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2004.

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Digital signal processing is used to focus the image and obtain a higher resolution than achieved by conventional radar systems. This paper presents details of Synthetic Aperture Radar (SAR) signal processing and imaging technique with the goal of generating images. A Matlab based program is developed and coded for imaging simulation. It facilitates processing data and producing desired output. Then, we investigate the characteristics of Linear Frequency Modulated (LFM) signal prior to getting image results. The SAR properties in range and azimuth directions are described. The received signal and SAR raw data is theoretically described. In addition, the target reflection signal processing is also well presented. The SAR image formation is described using Range Doppler Algorithm (RDA). Finally, simulation parameters are computed and imaging simulation test is finished.

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Maria Riasat. "Research on various image processing techniques." Open Access Research Journal of Chemistry and Pharmacy 1, no. 1 (2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.

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Wang, Jun Hong, Zong Rui Li, and Xi Bin Wang. "Application Research of Visual Processing Technology in the Industrial Production Line." Applied Mechanics and Materials 563 (May 2014): 338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.563.338.

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The CCD image sensor is set in the different production position, whose output signal is converted into digital signals to a dedicated image processing system by A/D. Using the image enhancement, smoothing, sharpening, segmentation, feature extraction, image recognition and understanding of digital image processing techniques,the system can identify the image, compare with feature information preservation, decide whether to enter the next process according to the similarity degree of alignment. Visual inspection having high precision, fast speed, working in the industrial field is stable and reliable, and improves the level of automation of production, make the products more competitive.

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Nicoll, G. R. "Digital Image Processing: a Practical Primer." IEE Proceedings F Communications, Radar and Signal Processing 132, no. 3 (1985): 202. http://dx.doi.org/10.1049/ip-f-1.1985.0046.

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Rajesh Kumar Upadhyay. "Digital Signal Processing: From Theory to Practical Applications." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2311–17. http://dx.doi.org/10.52783/tjjpt.v44.i4.1230.

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Digital Signal Processing (DSP) is a vital technology that bridges the gap between theoretical principles and practical applications in the digital age. This article explores the core components of DSP, emphasizing its theoretical foundations based on mathematical concepts like Fourier analysis, discrete-time signals, and the Nyquist theorem. It further delves into the practical applications of DSP, showcasing its extensive use in audio processing, image manipulation, telecommunications, biomedical diagnostics, and more. The article also outlines the challenges and future directions for DSP, including its integration with machine learning, quantum signal processing, and the development of efficient hardware solutions. DSP's potential in emerging fields like biological signal processing, data privacy, and sustainability is discussed, reflecting the ever-evolving nature of this technology. In conclusion, DSP is not just a technology but a dynamic force that continually reshapes our world by enhancing the quality of life, advancing science, and addressing global challenges.

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Serdenko, Taisiia, and Kateryna Liashko. "Signal coding based on wavelet analysis." Modeling and Information Systems in Economics, no. 103 (December 5, 2023): 188–96. https://doi.org/10.33111/mise.103.16.

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The article focuses on the analysis of the application of wavelet transforms in signal and image encoding. Wavelets are defined as a powerful tool in numerous technical and scientific disciplines, capable of effectively highlighting and processing signal characteristics at various levels of resolution. The article emphasizes the significance of wavelets in the encryption of images and signals, particularly regarding optimization of the encryption time and providing protection against various attacks. In today’s world, digital communication and data processing are gaining incredible importance, wavelet analysis is the key to success in numerous technical and scientific disciplines. This wavelet analysis article considers the meaning and application of wavelet analysis in signal coding. The history of wavelet analysis, its mathematical foundations, as well as modern methods and technologies that use this analysis to improve and optimize coding processes are considered. The value of wavelets in signal coding lies in their ability to efficiently extract and process signal characteristics at different levels of resolution. This versatility makes wavelets extremely useful in a wide range of applications, from image and video compression to cryptographic encryption and medical signal processing. This article examines the various ways in which wavelet transforms can improve signal coding, providing greater efficiency and security in a variety of applications, provides a deeper understanding of the role of wavelet analysis in modern signal coding. An innovative approach to encryption, which combines the Haar wavelet transform and «golden» matrices, opens up new possibilities in the cryptographic protection of digital signals. The article considers various approaches to the selection and application of wavelets for specific signal processing tasks, emphasizing their mathematical properties and practical effectiveness. The value of wavelets in the encryption of images and signals is reflected in the need to optimize encryption time and ensure protection against various attacks. Modern methods of wavelet coding need additional improvement to solve the task of processing different types of signals taking into account noise, frequency changes and other complexities.

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Sun, Mao Heng, Wei Jiang, Chen Hao Hu, and Tian Tian Feng. "Discussion on Excavation Monitoring Schemes Based on Image Processing." Applied Mechanics and Materials 71-78 (July 2011): 4317–20. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.4317.

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This study breaks through traditional displacement and inclination monitoring methods in excavation engineering, and proposes for the first time that combining excavation engineering with subjects such as Iconology, Signal Processing and Wireless Sensor Network, use Digital Signal Processing, Digital Image Processing, and Micro-Displacement Sensing technology for monitoring and processing various types of displacement. Then collect all monitoring points’ information through Internet of Things technology into database processing platform for processing, in order to achieve automatic real-time monitoring.

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Kumar, Ashwani, Paras Jain, Jabir Ali, Shrawan Kumar, and John Samuel Babu. "A lightweight buyer-seller watermarking protocol based on time-stamping and composite signal representation." International Journal of Engineering & Technology 7, no. 4.6 (2018): 39. http://dx.doi.org/10.14419/ijet.v7i4.6.20230.

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The protocol allows a content provider to detect duplicate copy of a digital content and restrict the content provider who blames the innocent customer. This paper, proposed a lightweight protocol, which uses composite signal representation and time-stamping for watermark embedding and extraction. We have used timestamp, which tells at what time the digital content was created, signed or verified to digital watermarking algorithms and uses the composite signal representation for minimizing the overhead and bandwidth due to the use of composite signals. The suggested protocol uses composite signal representations and timestamp based methods with digital watermarking scheme for content authentication. Our watermark embedding and detection algorithm achieves a balance between robustness and image visual quality. Simulation results demonstrate that the algorithm used by proposed protocol has an increase robustness and good quality of watermark images as well and withstand against various image-processing attacks.

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Wang, Chun Yu, Qing Wei Dong, Yang Li, and Xin Yue Xie. "Airborne Multispectral Imager Used in the UAV." Applied Mechanics and Materials 701-702 (December 2014): 283–87. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.283.

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Under existing conditions, the use of optical lens, imaging spectrometer, and the establishment of a rectangular CMOS image sensor spectral imaging system. Wide spectrum of white LED illuminated sample, by ordinary optical imaging lens, then the multi-channel narrowband filter array on the image plane splitting, and finally by the CMOS image acquisition system, converting optical signals into electrical signals, and then converted by the analog-digital converter chip to a digital signal to the computer. Finally, computer-spectral image cube collected for processing. At the same time, the advantages of airborne multispectral imager used in the UAV trials, obtain higher picture quality, and maintain a portable, imaging speed, and further validate the reliability of the experimental system.

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Scott, George. "Digital imagery for making plates." Journal of Micropalaeontology 14, no. 2 (1995): 118. http://dx.doi.org/10.1144/jm.14.2.118.

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Abstract. Although the resolution and depth of focus provided by scanning electron microscopy (SEM) revolutionized the examination of several groups of microfossils, conventional photographic techniques are normally outlined in instructions for preparation of micrographs for publication (Whittaker &amp; Hodgkinson, 1991). While the quality of results attainable by following these methods is very high, digital image recording and processing techniques are now well developed and readily available. This note outlines some advantages of digital techniques in the preparation of SEM images for publication.DIGITAL RECORDINGSecondary electron and other detectors attached to the SEM produce analogue (waveform) signals. In early instruments only these analogue signals were processed and displayed. Modern designs quantize signals from the detector as pixels (picture elements) which represent grey levels along scan lines. Pixel information is processed by the SEM on-board computer and saved as an image file. Importantly, the basic hardware to convert the analogue signal to digital form is simple and can be readily retro-fitted to early instruments. Our Philips PSEM 500 was adapted to record 128 grey levels at 800 pixels/line over 600 lines/frame, a minimum specification for professional work. Many micropalaeontologists will find that their SEM laboratories can supply digital files at higher resolutions. However, an essential point is to work with images recorded digitally directly from the SEM video channel, so avoiding potential degradation due to scanning of images recorded on film from the SEM monitors.DIGITAL PROCESSINGI use Photostyler (a PC image editor by Aldus Corp.) for plate composition. It resembles. . .

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Pankaj, Kumar Sinha, and Sharan Preetha. "Multiplexer Based Multiplications for Signal Processing Applications." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 583–86. https://doi.org/10.11591/ijeecs.v9.i3.pp583-586.

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In signal processing, Filter is a device that removes the unwanted signals. In any electronic circuits, Filters are widely used in the fundamental hands on tool. The basic function of the filter is to selectively allow the desired signal to pass through and /or control the undesired signal based on the frequency. A signal processing filter satisfies a set of requirements which are realization and improvement of the filter. A filter system consists of an analog to digital converter is used to sample the input signal, traced by a microprocessor and some components such as memory to store the data and filter coefficients. Filters can easily be designed to be ―linear phase‖ and it is easy to implement. In this paper, the birecoder multiplier (BM) is designed in terms of VLSI design environment. The proposed multiplier is implemented by using VHDL language and Xilinx ISE for synthesis. The multiplier is mainly used for image processing applications as well as signal processing applications.

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Yan, Gao, Yan Liang, Xin Zhou, and Chun Xia Qi. "A Digital Watermarking Algorithm Based on the Wavelet Bit Plane Coding." Advanced Materials Research 821-822 (September 2013): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amr.821-822.1438.

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In this paper a algorithm of digital image watermark based on wavelet bit plane is introduced, and the original image is not required for detecting the watermarking. The digital watermark is embedded by changing information of some bit planes in DWT images at different resolutions. The watermark can be extracted on the difference bit plane values of subimages of the decomposed watermarked image which is then mapped to an image with a few shades of gray. Experimental results show that the watermark is robust to several signal processing techniques, including JPEG compression and some image processing operations

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Louk, Andreas Christian, Gede Bayu Suparta, and Nurul Hidayah. "Image Processing for Multiple Micro-Radiography Images." Advanced Materials Research 896 (February 2014): 676–80. http://dx.doi.org/10.4028/www.scientific.net/amr.896.676.

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An image processing method has been developed for processing multiple images of x-ray micro-radiography. An x-ray micro-radiography image reflects quantum mottle so that its information content may tends to be corrupted. Therefore, a digital processing method has been developed to reduce the effect of quantum mottle as well as reducing the noise level. A set of radiographs are collected then summed. An image subtraction by a background image is carried out prior to the summation process. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) after processing are compared with the SNR and CNR prior to the processing. As a result the final image for small specimen under x-ray micro-radiography inspection is better than original image without processing based on SNR and CNR assessments.

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Wang, Xi, Taizheng Chen, Dongwei Li, and Shiqi Yu. "Processing Methods for Digital Image Data Based on the Geographic Information System." Complexity 2021 (June 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/2319314.

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Digital image data processing is mainly to input digital image data into a computer to complete the conversion of a continuous spatially distributed image model into a discrete digital model so that the computer can identify, process, and store the processing process of digital image information. Geographic information system (GIS) is a computer system that integrates multiple forms of information expression, and it integrates functions such as collection, processing, transmission, storage, management, analysis, expression, and query retrieval, which can quickly discover the spatial distribution of things and their attributes and can express the results accurately and vividly in various intuitive forms. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of processing methods for digital image data, elaborated the development background, current status, and future challenges of the GIS technology, introduced the methods and principles of permutation matrix algorithm and subimage averaging method, constructed the processing model for digital image data based on GIS, analyzed the data structure and its database establishment for digital image, proposed the processing methods for digital image data based on GIS, performed the enhancement processing and calculation classification of digital image data, and finally conducted a case analysis and its result discussion. The study results show that the proposed processing methods for digital image data based on GIS can perform analogue-to-digital conversion of continuous images, complete the steps of sampling, layering, and quantization, and then encode the obtained discrete digital signal into the computer to form an in-plane collection of pixels; this processing method can also organically combine spatial information and image data and identify, process, and store digital image data from both spatial and attribute aspects. The study results of this paper provide a reference for further research on the processing methods for digital image data based on GIS.

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Wu, Ling Fan, Li Jun Yun, Jun Sheng Shi, Kun Wang, and Zhi Hui Deng. "Design and Implementation of the HD Video Signal Converter Based on FPGA." Advanced Engineering Forum 6-7 (September 2012): 571–75. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.571.

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In this paper, based on the FPGA and with a video dedicated A / D converter chip, LVDS coding chip, the design and implementation of a SD(standard-definition) analog video signals to HD(high-definition) digital video signal converter. First, input SD analog video into digital video signals meet the ITU-BT656 standard. Then use the FPGA with the video processing chip and DDR do some corresponding processing to achieve high-definition digital video output. After the actual test, the converter output signal of the image quality is well, meets the design requirements, and to verify the effectiveness of the program.

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Ullah, Khalil, Khalil Khan, Muhammad Amin, Muhammad Attique, Tae-Sun Chung, and Rabia Riaz. "Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters." Applied Sciences 10, no. 15 (2020): 5099. http://dx.doi.org/10.3390/app10155099.

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Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

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Piva, Alessandro. "An Overview on Image Forensics." ISRN Signal Processing 2013 (January 10, 2013): 1–22. http://dx.doi.org/10.1155/2013/496701.

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The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes.

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Eshan, Khan *1 Deepti Rai 2. "IMAGE ENCRYPTION USING TRAPDOOR ONE WAY FUNCTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 8 (2018): 592–98. https://doi.org/10.5281/zenodo.1403406.

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In this paper introduction about the digital images and digital image processing is given. In every application of digital image processing highly encrypted and adaptive algorithm is needed. For the encryption and decryption of the images an adaptive pixel masking technique is used in our paper. For removing noises which affect the images during processing with the help of Linear and Space invariant filters. At long last the execution of the proposed image encryption-decryption calculation working in conjugation with the image restoration system has been assessed regarding PSNR and MSE.

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Rajabalipanah, Hamid, Ali Abdolali, Shahid Iqbal, Lei Zhang, and Tie Jun Cui. "Analog signal processing through space-time digital metasurfaces." Nanophotonics 10, no. 6 (2021): 1753–64. http://dx.doi.org/10.1515/nanoph-2021-0006.

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Abstract In the quest to realize analog signal processing using subwavelength metasurfaces, in this paper, we present the first demonstration of programmable time-modulated metasurface processors based on the key properties of spatial Fourier transformation. Exploiting space-time coding strategy enables local, independent, and real-time engineering of not only amplitude but also phase profile of the contributing reflective digital meta-atoms at both central and harmonic frequencies. Several illustrative examples are demonstrated to show that the proposed multifunctional calculus metasurface is capable of implementing a large class of useful mathematical operators, including 1st- and 2nd-order spatial differentiation, 1st-order spatial integration, and integro-differential equation solving accompanied by frequency conversions. Unlike the recent proposals based on the Green’s function (GF) method, the designed time-modulated signal processor effectively operates for input signals containing wide spatial frequency bandwidths with an acceptable gain level. Proof-of-principle simulations are also reported to demonstrate the successful realization of image processing functions like edge detection. This time-varying wave-based computing system can set the direction for future developments of programmable metasurfaces with highly promising applications in ultrafast equation solving, real-time and continuous signal processing, and imaging.

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Stefanović, Nenad, Boban Sazdić-Jotić, Vladimir Orlić, Vladimir Mladenović, and Stefan Ćirković. "Application of compressive sensing techniques for advanced image processing and digital image transmission." Bulletin of Natural Sciences Research, no. 00 (2024): 12. http://dx.doi.org/10.5937/bnsr14-51559.

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The field of compressive sensing (CS) has emerged as a transformative approach in the acquisition and processing of high-dimensional data. This paper presents a comprehensive study on the application of compressive sensing techniques to advanced image processing and digital image transmission. By leveraging the inherent sparsity in natural images, CS allows for significant reductions in the amount of data required for accurate reconstruction, thereby overcoming the limitations imposed by the traditional Shannon-Nyquist sampling theorem. We explore the theoretical foundations of CS, including the principles of sparsity and incoherence, and provide a detailed overview of the Orthogonal Matching Pursuit (OMP) algorithm, a prominent greedy algorithm used for sparse signal recovery. Experimental results demonstrate the efficacy of CS in improving image reconstruction quality, as evidenced by enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, we discuss the practical implementation of CS in single-pixel cameras and its potential impact on future imaging technologies. The findings suggest that CS offers a robust framework for efficient image acquisition and processing, making it a valuable tool for various applications in multimedia, medical imaging, and remote sensing.

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Du, Yu Jian, Zu Bin Chen, Teng Yu, and Yang Yang. "FIR Digital Filter Design Based on Virtual Instrument." Advanced Materials Research 684 (April 2013): 653–56. http://dx.doi.org/10.4028/www.scientific.net/amr.684.653.

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With the information era and the advent of the digital world, digital signal processing has become extremely important in today's one of the disciplines and technical fields.Digital signal processing in seismic signal ,communications, voice, image, automatic control radar, and other fields has been widely used.In this paper,I design several kind of FIR digital filters based on virtual instrument to solve the problem that signal noise reduction.

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Kim, Byung-Gyu. "Digital Signal, Image and Video Processing for Emerging Multimedia Technology." Electronics 9, no. 12 (2020): 2012. http://dx.doi.org/10.3390/electronics9122012.

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Sridhar, P., and . "A Robust Digital Image Watermarking in Hybrid Frequency Domain." International Journal of Engineering & Technology 7, no. 3.6 (2018): 243. http://dx.doi.org/10.14419/ijet.v7i3.6.14981.

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Image watermarking is a method to hide the secret information in a host image for copyright protection of watermark data during the transmission by means of insecure channel. The proposed scheme protects our data with adaptive level of visual quality and robustness against signal processing and geometric attacks. The proposed method divides the host image into four non-overlapping segments labelled as sub-images, DWT is applied on each sub images and then block based DCT is applied on mid frequency channels LH and HL of discrete wavelet transform. Embedded matrix is formed using hybrid transformed coefficients where matrix elements are chosen from the localized two mid frequency coefficients of each block in DCT. SV Decomposition is applied on embedded matrix to factorize it into singular values, left and right singular vectors and embed the scrambled watermark image along with scaling factor in singular value matrix. This repetition of watermark data in each sub-image reduces the PSNR values of the watermarked image. Despite this proposed scheme scales down PSNR value, changing the scaling factor favours to adjust the PSNR to the acceptable level and withstand the signal processing attacks such as JPEG compression and geometrical attack such as rotation, translation. Compared to the other method, the proposed scheme gives better correlation coefficient value for above mentioned kinds of attacks and also provide adaptive PSNR for imperceptibility on watermarked image.

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