Keywords : wavelet transform


Sound Signal De-noising Using Wavelet Transform

Yusra Faisal Al-Irhaim; Ahmad Mohammd Suliman

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 143-157
DOI: 10.33899/csmj.2012.163724

In this paper, the Discrete Wavelet Transform is studied in purifying the sound signal from noise because of the good capabilities in this scope, especially when it is merged with both types of the (Thresholding), the solid and the flexible. The aim of this research is making comparisons between the types of deferent  Discrete Wavelet Transform for both the filters which are used (Harr, Daubechies) in deferent levels (2, 3, 4, 5) with the additive of two types of noise to these filters (Gaussian White Noise) and (Random Noise). The decibel value that added to these filters was in the (5dB,10dB,15dB) values. Good results of the purification process are achived after computing the (Signal to Noise Ratio (SNR)) and (Mean Sequre Error (MSE)).
 
 

A New Method for Iris Segmentation and Feature Extraction in Spatial Domain

Dujan B. Taha; Shamil Q. Ibraheem

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 1, Pages 35-46
DOI: 10.33899/csmj.2012.163669

In this paper, a new proposed method is developed, this method contains two algorithms, one for human iris recognition, the other is for extracting the features of the recognized iris.
Many studies tried to extract the iris from images, most of those studies succeeded but after using very complex processing and filtering operations in addition to transforming the image to other domains (such as frequency domain) to achieve the required operations. The proposed method deduct those complexities operations to a minimum and requires only wavelet transform in a small part of the second algorithm, all other operations will be applied directly to the spatial domain. Experimental results shows the efficiency of the method as its being applied to the images of the Chinese Academy of Sciences – Institute of Automation “CASIA” iris database which contains a lot of deformations. Finally, Matlab R2010a (Ver. 7.10.0) was used to implement the algorithms presented in this paper because it facilitates handling images, arrays, and filters.
 

Encryption and Hiding Watermarking Using A Chaotic Modified Wavelet Transform

Shaimaa Sh. Mohammad

AL-Rafidain Journal of Computer Sciences and Mathematics, 2011, Volume 8, Issue 2, Pages 87-97
DOI: 10.33899/csmj.2011.163645

In this letter, a new digital watermarking algorithm is proposed to hide binary image watermark inside gray image to increase authentication and robustness, DWT are used in embedding because it has many features in image processing. To increase the effective of this algorithm chaotic logistic map are used to select the embedding position in host chaotically which increase the security and make it hard to be detected, experimental result which be measured by using (Mean Square error, Peak signal to noise ratio , Signal to Noise Ratio, CORLLATION) has value (0.3195, 53.0867, 45.9152, 0.9998) respectively reflect the effective of this algorithm, the watermarked image has no noticeable change, some image processing operation such as(filter ,noise and compression) are used to test the robustness which measured by calculate similarity between extracted watermark after attack and the original one, Programming based on Matlab9.
 

Iris Recognition System Based on Wavelet Transform

Maha A. Hasso; Bayez K. Al-Sulaifanie; Kaydar M. Quboa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 2, Pages 105-116
DOI: 10.33899/csmj.2009.163801

In order to provide accurate recognition of individuals, the most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template. In this paper, the feature extraction techniques are improved and implemented. These techniques are using wavelet filters. The encoded data by wavelet filters are converted to binary code to represent the biometric template. The Hamming distance is used to classify the iris templates, and the False Accept Rate (FAR), False Reject Rate (FRR)  and recognition rate (RR)  are calculated [1]. 
            The wavelet transform using DAUB12 filter proves that it is a good feature extraction technique. It gives equal FAR and FRR  and a high recognition rate for the two used databases. When applying the DAUB12 filter to CASIA database, the FAR and FRR are equal to 1.053%, while the recognition rate is 97.89%. For Bath database the recognition rate when applying DAUB12 filter is 100%. CASIA and Bath databases are obtained through personal communication. These databases are used in this paper.
 

Simulation of Real Time 2D DWT Structure

Mohanad L. Ahmad

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 41-48
DOI: 10.33899/csmj.2009.163764

This research contains an introduction to the first works in this field, and describes the Discrete Wavelet Transform DWT which is the only kind that can be implemented in the digital computer. The research lists the important structures that are used to implement the digital filters which are the hart of DWT.  The research contains problems that face us in my M.Sc thesis to implement DWT image structure processor. In my M.Sc thesis we solve the problem of zero padding but we solve the second problem (waiting the column processor until the row processor finish its process) by using pipelineing technique. The pipeline technique solves the problem partially. This research solve the second problem completely by proposing a new structure which make the Two Dimentional DWT (2D-DWT) structure process the video in real time without waiting the row processor.
The research was build fast structure that can decompose image by using DWT. The speed performance of the structure was tested using (Simulink) in (Matlab7).
The results obtained from simulation of the proposed 2D DWT structure are compared with my M.Sc thesis structure and the traditional structure to show how we improve the process speed of 2D DWT structure.