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Implementation of Image Denoising Techniques using Novel Slantlet Transform

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Hitha M., Rudresh M.D., R. Sundaraguru

Hitha M., Rudresh M.D. and R Sundaraguru. Implementation of Image Denoising Techniques using Novel Slantlet Transform. International Journal of Computer Applications 183(11):15-21, June 2021. BibTeX

	author = {Hitha M. and Rudresh M.D. and R. Sundaraguru},
	title = {Implementation of Image Denoising Techniques using Novel Slantlet Transform},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {11},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {15-21},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2021921416},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Wavelets have been developed to analyze the frequency components of a signal according to a scale. They provide more information than the Fourier transform for signals which have discontinuities or sharp spikes. The modern techniques of digital signal processing such as MultiMate filtering, sub band coding and wavelet transform have been studied and applied effectively in science and technology fields nowadays. The discrete wavelet transform (DWT) is usually carried out by filter bank iteration; however, “for a fixed number of zero moments, this does not yield a discrete-time basis that is optimal with respect to time localization”. This project focuses on the “implementation and properties of an orthogonal DWT, with two zero moments and with improved time localization (wavelet bases generation)”, determining the relation between this transform and M –band wavelet theory, and its application in image coding is implemented. Shorter the scaling function spectrum, larger the number of wavelet co-efficient and hence more scale information (by designing filter of shorter length).The wavelet basis function is not on filter bank iteration; but on, different filters for each scale. Moment vectors are calculated based on input (either 1 – D or 2 – D Signal) signal and it is projected on wavelet basis to extract details of signal by using multi resolution analysis. The decomposition level is adapted to the length of signal as in case of fixed level in traditional discrete wavelet transform. For coarse scales, the support of the discrete-time basis function reduced (by a factor approaching one third for coarse scales).The implementation and properties of an orthogonal DWT, with two zero moments and with improved time localization are discussed in this project work. The wavelet representation of images using slantlet basis function is presented. The slantlet filter bank (wavelet bases generation) design technique where different filters for each level (scale or stage) is described will be implemented in this project work. The application of an image denoising using orthogonal discrete wavelet (slantlet) transform is presented in this project work. The various threshold methods which are used for image denoising is also discussed in this work. The signal estimation technique from the observed signal (that is corrupted by noise) that exploits the capabilities of wavelet (slantlet bases) transform for signal denoising is implemented in this project. The soft threshold technique which is useful in image enhancement coding (where noisy co-efficient are killed by fixing the threshold level) is also investigated.


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Wavelet Transform, Slantlet Transform, image denoising technique, soft thresholding, DWT, SLT