A fine-grain load-adaptive algorithm of the 2D discrete wavelet transform for multithreaded architectures

2004 ◽  
Vol 64 (1) ◽  
pp. 68-78 ◽  
Author(s):  
Parimala Thulasiraman ◽  
Ashfaq A. Khokhar ◽  
Gerd Heber ◽  
Guang R. Gao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Adaptive Algorithm ◽  
Discrete Wavelet ◽  
Multithreaded Architectures ◽  
Fine Grain

scholarly journals Discrete wavelet transform-based RI adaptive algorithm for system identification

2020 ◽  
Vol 10 (3) ◽  
pp. 2383
Author(s):  
Mohammad Shukri Salman ◽  
Alaa Eleyan ◽  
Bahaa Al-Sheikh
Keyword(s):  
Wavelet Transform ◽  
System Identification ◽  
Convergence Rate ◽  
Discrete Wavelet Transform ◽  
Adaptive Algorithm ◽  
Convergence Rates ◽  
Recursive Least Squares ◽  
Discrete Wavelet ◽  
Step Size ◽  
Autocorrelation Matrix

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

scholarly journals Implementation of Efficient Architecture of Fine Grain Pipelined Lifting Scheme Based Two Dimensional Discrete Wavelet Transform

2019 ◽  
Vol 8 (12) ◽  
pp. 225-229
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Speech Processing ◽  
Video Processing ◽  
Critical Path ◽  
Lifting Scheme ◽  
Practical Implementation ◽  
Discrete Wavelet ◽  
Hardware Complexity ◽  
Fine Grain

Many Discrete Wavelet Transform (DWT) based VLSI architectures have been projected to meet the necessities of the synchronized signal processing. It includes image processing, speech processing, signal and video processing, etc. The practical implementation of DWT has fewer hitches in terms of hardware complexity and memory requirement since it needs to process huge volume of data. The traditional convolution based system needs more multipliers and larger memory and is also not suitable to provide speed or power efficient image or video processing designs. The lifting scheme involves very few mathematical computations compared to the convolution-based DWT. In this paper, we propose an architecture that performs Discrete Wavelet Transform (DWT) using a lifting-based scheme with fine grained pipelined architecture. The basic DWT filters used in image compression are 5/3(lossless) and 9/7(lossy) filters. In fine grain pipelining, multiplier is split into two units by placing the latches on the horizontal cutset across the multiplier. Thus the critical path is reduced to half of the multiplier delay. As a result, it is a speed efficient architecture and is symmetrical with a lower hardware complexity. The architecture is designed using verilog HDL and implemented on Xilinx Spartan 3E FPGA.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization

An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
Author(s):  
Chih-Hsien Hsia ◽  
Ting-Yu Lin ◽  
Jen-Shiun Chiang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Document Image ◽  
Background Information ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Image System ◽  
Low Frequencies ◽  
Cost Efficient ◽  
Binarization Method

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.

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