scholarly journals Comparison between Orthogonal and Bi-Orthogonal Wavelets

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Ali Naji Shaker
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Wavelet Transform ◽  
Digital Signal Processing ◽  
Signal Reconstruction ◽  
Digital Signal ◽  
Biorthogonal Wavelet ◽  
Biorthogonal Wavelets ◽  
Orthogonal Wavelet ◽  
Orthogonal Wavelets

Theoretical aspects and analysis of wavelets have applications in mathematical modeling, artificial neural networks, digital signal processing, and image processing and numerical methods. The term orthogonal deals with the mathematical part which covers a wide area of digital signal processing and image processing. An orthogonal wavelet generates the wavelet whose nature is orthogonal. This means an inverse or transpose wavelet transform is nothing but the adjoint of a wavelet transform. If this condition fails by missing orthogonality it may result in biorthogonal wavelets. Single scaling functions and single wavelets are generated but orthogonal wavelet filter bank. A biorthogonal wavelet associated with the wavelet transformation is invertible. There is no need that if it is invertible so it should be orthogonal. The biorthogonal wavelet allows maximum freedom in the case of designing the orthogonal wavelet. It also supports the construction of symmetric wavelet functions. In biorthogonal wavelets, as the name indicates, two scaling factors or functions are responsible for the generation of the various multi-resolutions on the basis of different wavelets. For the image and signal reconstruction purpose we need that wavelet. We get a better result in the presence of biorthogonal wavelets. In the present work, we analyzed the performance of orthogonal and biorthogonal wavelet filters for image processing. We test the image and observed that the filter coefficient and image quality for the orthogonal and biorthogonal wavelet. On the basis of performance analysis it is concluded that biorthogonal wavelets are better than orthogonal wavelets.

Approximate Compressor-Based Multiplier Design Methodology for Error-Resilient Digital Signal Processing

2020 ◽  
Vol 29 (14) ◽  
pp. 2050233
Author(s):  
Zhixi Yang ◽  
Xianbin Li ◽  
Jun Yang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Electrical Performance ◽  
Approximate Computing ◽  
Dsp Applications

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25[Formula: see text]dB for image processing; similarly, with a decrease of 0.3[Formula: see text]dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs.

Applications in Digital Image Processing

2013 ◽  
Vol 107 (1) ◽  
pp. 46-53 ◽  
Author(s):  
Jason Silverman ◽  
Gail L. Rosen ◽  
Steve Essinger
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Popular Culture ◽  
Digital Signal Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Signal

Use digital signal processing to capitalize on an exciting intersection of mathematics and popular culture.

Xputer use in image processing and digital signal processing

1990 ◽  
Author(s):  
Reiner W. Hartenstein ◽  
A. G. Hirschbiel ◽  
K. Lemmert ◽  
M. Riedmueller ◽  
Karin Schmidt ◽  
...  
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Signal

A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities

2018 ◽  
Vol 15 (1) ◽  
pp. 66-73 ◽  
Author(s):  
Mashal Tariq ◽  
Ayesha A. Siddiqi ◽  
Ghous Baksh Narejo ◽  
Shehla Andleeb
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Signal Processing ◽  
Magnetic Resonance ◽  
Digital Signal Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Signal ◽  
Imaging Modalities ◽  
Resonance Imaging

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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