Multiresolution analysis based on 2-D **discrete** **wavelet** **transform** was used in our algorithm for the development of computer aided detection of microcalcifications to help radiologists in the diagnosis of breast cancer. In this paper, we studied in a first stage the optimal level of decomposition and we chose the **wavelet** which appears most suitable with our problems. This study showed that from a certain level of decomposition, we lose information which corresponds to the microcalcifications. In the second stage, we perform a multiresolution decomposition filter banks of the mammograms. The binarisation of the reconstituted image as well as the local thresholding gave a clear reduction in the number of false positives.

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The digital data can be transformed using **Discrete** **Wavelet** **Transform** (DWT). The **Discrete** **Wavelet** **Transform** (DWT) was based on time-scale representation, which provides efficient multi-resolution. The lifting based scheme (9, 7) (Here 9 Low Pass filter coefficients and the 7 High Pass filter coefficients) filter give lossy mode of information. The lifting based DWT are lower computational complexity and reduced memory requirements. Since Conventional convolution based DWT is area and power hungry which can be overcome by using the lifting based scheme. The **discrete** **wavelet** **transform** (DWT) is being increasingly used for image coding. This is due to the fact that DWT supports features like progressive image transmission (by quality, by resolution), ease of transformed image manipulation, region of interest coding, etc. DWT has traditionally been implemented by convolution. Such an implementation demands both a large number of computations and a large storage features that are not desirable for either high-speed or low-power applications. Recently, a lifting-based scheme that often requires far fewer computations has been proposed for the DWT. In this work, the design of Lossy 3-D DWT (**Discrete** **Wavelet** **Transform**) using Lifting Scheme Architecture will be modeled using the Verilog HDL and its functionality were verified using the Modelsim tool and can synthesized by using the Xilinx tool.

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2) D **Discrete** **Wavelet** **Transform**: The **Discrete** Wavelets **Transform** (DWT) [39], transforms a **discrete** time signal to a **discrete** **wavelet** representation. Initially, the **wavelet** parameters are discretized to reduce the continuous basis set of wavelets to a **discrete** and orthogonal/ orthonormal set of basis wavelets. The 1D DWT is given as the inner product of the signal x(t) being transformed with each of the **discrete** basis functions.

Vinay U. Kale & Nikkoo N. (2010) et al. [9] explains **Discrete** Cosine **Transform**, **Discrete** **Wavelet** **Transform**. The **discrete** cosine **transform** (DCT) helps separate the image into parts (or spectral sub-bands) of differing importance (with respect to the image's visual quality). The DCT is similar to the **discrete** Fourier **transform**: it transforms a signal or image from the spatial domain to the frequency domain. The **discrete** **wavelet** **transform** (DWT) refers to **wavelet** transforms for which the wavelets are discretely sampled. The **transform** is based on a **wavelet** matrix, which can be computed more quickly than the analogous Fourier matrix. Most notably, the **discrete** **wavelet** **transform** is used for signal coding, where the properties of the **transform** are exploited to represent a **discrete** signal in a more redundant form, often as a preconditioning for data compression. It was also concluded that the human eye is fairly good at seeing small differences in brightness over a relatively large area, but not so good at distinguishing the exact strength of a high frequency brightness variation. This fact allows one to get away with greatly reducing the amount of information in the high frequency components. This is done by simply dividing each component in the frequency domain by a constant for that component, and then rounding to the nearest integer. This is the main lossy operation in the whole process. As a result of this, it is typically the case that many of the higher frequency components are rounded to zero, and many of the rest become small positive or negative numbers.

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As shown in the Fig 1 the block diagram of **wavelet** based encoder and decoder for compression of images. The first stage is encoding stage for compressing and second stage is decoding for reconstruction of the image. at the encoding stage the image is performed with **Discrete** **wavelet** **transform** with 'db4' **wavelet** **transform**. This transformation gives the decomposition of original image into frequency bands. as shown in Fig 2 The LL coefficients are called approximate coefficients ,where the most of the energy is concentrated and much of the information about the image is available. The LH, HL, HH are called the detail coefficients, and the information content is less significant. The image compression is achieved by truncation of these detail coefficients and the different strategies will give different algorithms [10]. In Literature several approaches evolved for different strategies. The limitations like complexity to be considered for applying algorithms to compress satellite images. Hence only one level of decomposition is considered. After decomposition the data is routed to Thresholding stage and quantization stage, this thresholding is done according to the equation 6 which truncates the less significant coefficients to zero which are nearer to zero. The absolute value of the coefficients should be taken for truncation .This truncation increases the number of zero values in the stream which allow for encoding to get compression. Fig 6 shows the histogram of truncated coefficients for band 5 with a threshold value of 2000,The histogram as shown in the figure is shifted toward right, hence the index of the peaked value in the **wavelet** bin represents zero..The Threshold value is adapted to get different bitrates. To achieve quantization 256 levels are used . After quantization the data is routed for Encoding, several encoding codes [11] [12]are available for encoding ,Huffman code is one popular among them but the computational complexity to be considered for implementation of satellite image compression.Hence a simple variable length code is used for encoding.

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In the applications of multi rate filter banks, a bank of analysis filters is applied to a **discrete** input signal and then down sampled at fixed rate to produce a set of sub-band signals. If a dual bank of synthesis filters exists, by means of which the original input signal can be recovered by first up sampling each of the above sub-band signals and then applying it to a synthesis filter, then the two filter banks are said to be a perfect reconstruction (PR) pair of filter banks. The term uniform filter bank (UFB) is used to emphasize that all the sub-band signals are down sampled at the same rate. PR pair of **wavelet** analysis and synthesis filter banks is dual. The **discrete** **wavelet** **transform** (DWT), and multi resolution analysis, can be viewed as the application of a non-uniform filter bank, defined by a UFB. In terms of **wavelet** theory, a low-pass filter corresponds to scaling function and the subsequent high-pass or band-pass filter corresponds to **wavelet** function. The DWT computation involves repetitive application of UFB on the low-pass channel.

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The **Discrete** **Wavelet** **Transform** (DWT) has become a very versatile signal processing tool over the last decade. It has been effectively used in signal and image processing applications. The advantage of DWT over other traditional transformations is that it performs multire solution analysis of signals with localization both in time and frequency. The DWT is being increasingly used for image compression today since it supports features like progressive image transmission, image manipulation, region of interest coding, etc. The coding efficiency and the quality of image restoration with the DWT are higher than those with the traditional **discrete** cosine **transform**. Furthermore, it is easy to attain a high compression ratio. So the DWT is widely used in signal processing and image compression, such as MPEG-4, JPEG 2000, and so on [1], [2]. Traditional DWT architectures [3], [4] are based on convolutions. Then, the second-generation DWTs, are based on lifting algorithms are proposed [5], [6]. Compared with convolution-based, lifting-based architectures require lesser computation complexity and also require less memory. Directly mapping these algorithms to hardware [7] leads to relatively long data path and low efficiency.

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fabric faults of various types. The hardware platform and software is developed for solving this problem. In our project vertical yarn missing, horizontal yarn missing, oil stain and hole, such defects are detected using **Discrete** **Wavelet** **Transform** and KNN classifier. This system is introducing texture defect detection using decomposition of defective and defect free images. The system acquires the image by using image acquisition device. This system based on MATLAB R2017b(9.3.0.713579) software.

ABSTRACT: Conventional distributed arithmetic (DA) is popular in field programmable gate array (FPGA) design, and it features on-chip ROM to achieve high speed and regularity. In this paper, we describe high speed area efficient 1-D **discrete** **wavelet** **transform** (DWT) using 9/7 filter based distributed arithmetic (DA) Technique. Being area efficient architecture free of ROM, multiplication, and subtraction, DA can also expose the redundancy existing in the adder array consisting of entries of 0 and 1. This architecture supports any size of image pixel value and any level of decomposition. The parallel structure has 100% hardware utilization efficiency.

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There will be a total of J estimations of the illuminant colour resulted from J -level DWT decomposition. However, selecting the correct estimation is not trivial. When the **wavelet** coefficients at finer scale j − 1 provides statistically strong features for illuminant colour estimation, due to the nature of **discrete** **wavelet** **transform**, one expects the similar estimation at very coarse scale j. Meanwhile, the estimation in finer scale is more prone to errors caused from fluctuations on **wavelet** coefficients. Due to multiscale decomposition, it is expected that the illuminant colour estimation at scale j is similar to the estimation at scale j − 1. Thus the difference in illuminant colour estimations between consecutive scales can be used to select the optimum scale where the estimations in consecutive scales, i.e., j and j − 1, have minimum angular error. The difference δ j,j−1 between two normalized illuminant colour estimations ˆ e j and ˆ e j−1 from scales j and

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First, each frame is transformed into various sub-bands via **discrete** **wavelet** **transform** (DWT) and then one robust sub-band is selected, here the robust sub-band is diagonal part. Watermark embedding[10], is done into the diagonal sub-band. A reliable watermark extraction is developed for extracting watermark from distorted images. Experimental results and comparison with PSNR values shows the robustness and the better performance of the proposed algorithm. Before introducing proposed video watermarking scheme, the requirements of an effective watermarking scheme and the related work are depicted first.

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ABSTRACT: Digital Signal processing ranks among the most demanding applications of digital design concepts. It is a mature technology domain wherein the demands for enhanced performance and resource utilization have risen exponentially over the years. Finite impulse response (FIR) filters are used in Digital Signal Processing applications.Accuracy in Filter Designing is based on the Multiplication and accumulation of filter coefficients. This paper describes an approach to the VLSI implementation of digital filter which is flexible and provides superior to traditional approaches,low power, and area efficient **Discrete** **Wavelet** **Transform** architecture.

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Abstract— in this paper, our system propose to recognize and classify the currency notes by using different steps starting from image acquisition, preprocessing, testing, training. Methodology used for feature extraction is **Discrete** **Wavelet** **Transform** (DWT) and approximate coefficient matrix of the currency image is derived. Statistical features are extracted using coefficient and stored in a vector. Extracted features are used to classify the currency note using Probabilistic Neural Network [1].

Abstract— This paper presents an approach of edge detection by multi-scale undecimated **discrete** **wavelet** **transform**. Haar **wavelet** is used for this detection which models the theorical representation of an edge. The advantage of this approach is the combination between filtering step and detection one. To consolidate the result of our method we present a measure of edge localization’s by calculating distance from a real square edge’s and the detection’s result of our approach compared to canny detector’s result.

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Image processing and analysis based on the Continuous or **Discrete** Image Transforms are classic techniques. Amongst these, **Discrete** Image **Transform** is widely used in image filtering, data description, etc. Also, the **Wavelet** Theorem makes up very popular methods of image processing, de-noising and compression. Considering that the Haar functions are the simplest wavelets, these forms are used in many methods of **Discrete** Image Transforms and processing. In this article, it has been shown how the proper implementation of **Discrete** **Wavelet** **Transform** using Haar functions can be beneficial in extracting the edges of a processed sample footprint thus required in a Footprint Analysis Program for crime detection.

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This paper presents FPGA design architecture for speech compression by using **discrete** **wavelet** **transform**. Db4 **wavelet** was chosen in this design work. The speech compression was achieved by keeping only the approximation components of each DWT process and throwing away the high pass filtered components. This can be done because most of the speech energy concentrates on the low frequency part. This design work successfully interfaced with the SDRAM chip, and the WM8731 codec chip. The compressed speech signal was read back after upsampling was performed. 3-level DWT filter banks were implemented and the resulting compressed speech could be heard clearly. At the same time, some background noise was introduced in the final data. Future work is to reduce noise.

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Fundamentally the medicinal imaging transfers on more precision without much data misfortune. The **Discrete** **Wavelet** **Transform** (DWT) is set up on time-scale reintroduction, which outfits powerful multi- resolution.This work learns that the picture pixel values given to the DWT operation which gives way the high pass and low pass coefficients of the input picture less engineering plan contrast with the customary usage of the DWT can de accomplished, little change changes expands the throughput contrast with that of the past strategies The recreation consequences of DWT were acknowledged with the suitable test cases. Once the useful check is done, **discrete** **wavelet** change is blended by utilizing Xilinx device.

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ABSTRACT: Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of **discrete** **wavelet** **transform** for feature extraction of power quality disturbance signal as a powerful tool for detecting of power quality disturbance. This paper employs a **discrete** **wavelet** **transform** (DWT) to have a better power quality disturbance detection accuracy. The disturbances of interest include voltage sag, voltage swell, transient, fluctuation, interruption and normal. The **discrete** **wavelet** **transform** has been used to detect and analyse the power quality disturbances. The disturbances of interest includes voltage sag, voltage swell, transient, interruption, fluctuation, and normal disturbance. The system is modelled using MATLAB Simulink. The disturbance voltage waveform is obtained from disturbance generation model. The DWT has been chosen for feature extraction. The outputs of the feature extraction are the DWT coefficients (detailed and approximate) represents the power quality disturbance signal at different levels in time and frequency domain.

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Digital image is represented as a two-dimensional array of coefficients, each coefficient representing the intensity level at that coordinate. Most natural images have smooth color variations, with the fine details being represented as sharp edges in between the smooth variations. Technically, the smooth variations in color can be termed as low frequency variations, and the sharp variations as high frequency variations. Separating the smooth variations and details of the image can be performed in many ways. One way is the decomposition of the image using the **discrete** **wavelet** **transform**. Digital image compression is based on the ideas of sub-band decomposition or **discrete** **wavelet** transforms. Wavelets, which refer to a set of basis functions, are defined recursively from a set of scaling coefficients and scaling functions. Image Segmentation is a crucial step in the conversion process for paper document images into electronic documents. Entities in a document image, such as text blocks, and figures need to be separated before further document analysis and recognition can occur. Many Document Segmentation algorithms are designed exclusively for a few specific document types, utilizing highly specialized document models.

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