The de-noising is a challenging task in the field of signal and image processing. Any natural image corrupted by gussian noise can be de-noised using wavelet method. Wavelet-based image denoising is an important technique in the area of image noise reduction. Wavelets have their natural ability to represent images in a very sparse form which is the foundation of wavelet-based denoising through ...

The proposed binarization is a scheme of parting a image pixel values into two classes black as foreground and white pixels as background then the thresholding is found for well known scheme for document image binarization. In this proposed work for the decomposition of both global and local thresholding this basic thresholding value we can use further. Here the global thresholding scheme is ef...

Image segmentation is one of the important tasks in computer vision and image processing. Thresholding is a simple but most effective technique in segmentation. It based on classify image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold. Otsu technique is a robust and fast thresholding techniques for most real world images w...

In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. In this work it was applied a generalized entropy formalism that represents a recent development in statistical mechanics. We propose, for the first time, an image thresholding method using a nonextensive entropy regarding the presence of nonadditive information content in some image ...

In this paper, we present discriminative approaches to histogram-based image thresholding, in which the optimal threshold is derived from the maximum likelihood based on the conditional distribution p(y|x) of y, the class indicator of a grey level x, given x. The discriminative approaches can be regarded as discriminative extensions of the traditional generative approaches to thresholding, such...

Denoising techniques that are based on modifying the transform of an image are considered here. In these techniques, a reversible, linear transform (such as transforms discussed in Chapter 2) is used to map the noisy image into a set of transform coefficients, which are then filtered using a suitable thresholding technique. Fig. 4.1 shows a typical denoising system that uses transform technique...

Multilevel image thresholding and clustering, two extensively used processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach yielding multiple threshold values for each color channel generate clustered segmented images appears be quite efficient it provides significant performance, although this method is computationally heavy. To...