scholarly journals Wavelet Transform for Forward and Inverse

2013 ◽  
pp. 153-156
Author(s):  
Karimella Vikram
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arithmetic Operation ◽  
Data Communication ◽  
Control Unit ◽  
Discrete Wavelet ◽  
Simulation Environment ◽  
Hardware Requirement ◽  
Intermediate Results ◽  
Address Generation

In this research, an architecture that performs both forward and inverse lifting-based discrete wavelet transform is proposed. The proposed architecture reduces the hardware requirement by exploiting the redundancy in the arithmetic operation involved in DWT computation. The proposed architecture does not require any extra memory to store intermediate results. The proposed architecture consists of predict module, update module, address generation module, control unit and a set of registers to establish data communication between predict and update modules. The symmetrical extension of images at the boundary to reduce distorted images has been incorporated in our proposed architecture as mentioned in JPEG2000. This architecture has been described in VHDL at the RTL level and simulated successfully using Model Sim simulation environment.

scholarly journals An efficient architecture for discrete wavelet transform and best-basis algorithm for images

2021 ◽  
Author(s):  
Aroutchelvame Mayilavelane
Keyword(s):  
Wavelet Transform ◽  
Cost Function ◽  
Discrete Wavelet Transform ◽  
Arithmetic Operation ◽  
Data Communication ◽  
Control Unit ◽  
Hardware Acceleration ◽  
Research Field ◽  
Discrete Wavelet ◽  
Best Basis Algorithm

The hardware acceleration of the wavelet transform for real-time systems has become an essential research field. In the first part of the thesis, an efficient architecture that performs both forward and inverse lifting-based discrete wavelet transform is proposed. The proposed architecture reduces the hardware requirement by exploiting the redundancy in the arithmetic operation involved in DWT computation. The proposed architecture consists of predict module, update module, address generation module, control unit and a set of registers to establish data communication between predict and update modules. The symmetrical extension of images at the boundary to reduce distorted images has been incorporated in our proposed for both (5,3) wavelet and (9,7) wavelet. Best-basis algorithm that is designed for signal compression and de-noising uses WPT to select the best-basis node for a given additive cost function. In the second part of the thesis, we propose wavelet architecture to perform WPT decomposition. A new algorithm to implement the natural logarithm function using Maclaurin series is proposed to implement the cost function used for best-basis algorithm. These architectures have been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment. These architectures are implemented in Virex ll Pro FPGA series of Xilinx.

scholarly journals An efficient architecture for discrete wavelet transform and best-basis algorithm for images

2021 ◽  
Author(s):  
Aroutchelvame Mayilavelane
Keyword(s):  
Wavelet Transform ◽  
Cost Function ◽  
Discrete Wavelet Transform ◽  
Arithmetic Operation ◽  
Data Communication ◽  
Control Unit ◽  
Hardware Acceleration ◽  
Research Field ◽  
Discrete Wavelet ◽  
Best Basis Algorithm

The hardware acceleration of the wavelet transform for real-time systems has become an essential research field. In the first part of the thesis, an efficient architecture that performs both forward and inverse lifting-based discrete wavelet transform is proposed. The proposed architecture reduces the hardware requirement by exploiting the redundancy in the arithmetic operation involved in DWT computation. The proposed architecture consists of predict module, update module, address generation module, control unit and a set of registers to establish data communication between predict and update modules. The symmetrical extension of images at the boundary to reduce distorted images has been incorporated in our proposed for both (5,3) wavelet and (9,7) wavelet. Best-basis algorithm that is designed for signal compression and de-noising uses WPT to select the best-basis node for a given additive cost function. In the second part of the thesis, we propose wavelet architecture to perform WPT decomposition. A new algorithm to implement the natural logarithm function using Maclaurin series is proposed to implement the cost function used for best-basis algorithm. These architectures have been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment. These architectures are implemented in Virex ll Pro FPGA series of Xilinx.

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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