A great challenge in the field of image processing nowadays, is image denoising. Although, there have been proposed various methods and algorithms for the same, but, most of them have not attained the desirable results. The performance does not match with the assumed one. The wavelet theory is relatively the newest concept in this field. The main aim of this study (paper) is to examine various ...

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The wavelet transform is a comparatively new and fast developing method for analysing signals. The main advantage of applying the wavelet transform to the detection of edges in an image is the possibility of choosing the size of the details that will be detected. The size of detected edges is set by the wavelet scale. In the case of the discrete wavelet transform the choice of the scale is perf...

Digital watermarking is an effective way to protect copyright. In this paper, a robust watermarking algorithm based on wavelet transformation is proposed which can confirm the copyright without original image. The wavelet transformation technique is effective in image analyzing and processing. Thus the color-image watermark algorithm based on discrete wavelet transformation (DWT) begins to draw...

Non-stationary signal processing applications use standard non-redundant DWT (Discrete Wavelet Transform) which is very powerful tool. But it suffers from shift sensitivity, absence of phase information, and poor directionality. To remove out these limitations, many researchers developed extensions to the standard DWT such as WP (Wavelet Packet Transform), and SWT (Stationary Wavelet Transform)...

Shift variance and poor directional selectivity, two major disadvantages of the discrete wavelet transform, have previously been circumvented either by using highly redundant, non-separable wavelet transforms or by using restrictive designs to obtain a pair of wavelet trees with a transform-domain redundancy of 4.0 in 2D. In this paper, we demonstrate that excellent shift-invariance properties ...

PURPOSE The wavelet transform is a newly developed signal-processing tool that decomposes a signal into various levels of resolution. The wavelet transform based noise reduction has the characteristics of optimally separating signal from noise, preserving the rapid rises and falls of a signal, and reconstructing a smooth signal from noise-imposed observations. The aim of this study was to evalu...

Image registration is an important and fundamental task in image processing used to match two different images. Given two or more different images to be registered, image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A feature-based approach to automated imageto-image registration is presented. The characte...

Medical images are corrupted by noises during its transmission and acquisition process. Noise reduction has been a traditional problem in image and signal processing. Medical images generally contains minute information about heart, brain, nerves etc therefore wrong diagnosis might not rescue the patient from harmful effects. In this paper we proposed an approach for image denoising based on wa...

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...