Thresholding is an important approach for image segmentation and it is the first step in the image processing for many applications. Segmentation is a low level operation that can segment an image in nonoverlapping regions. The optimal thresholds are found by maximizing Kapur's entropy-based thresholding function in a grey level image. However, the required CPU time increases exponentially with...

Image thresholding is a common task in many computer vision and graphics applications. The goal of thresholding an image is to classify pixels as either “dark” or “light”. Adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination. We present a technique for real-time adaptive thresholding using the integral image of the input. Our technique is an...

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...

This work addresses the recovery of an image from its multiple noisy copies. The standard method is to compute the weighted average of these copies. Since the wavelet thresholding technique has been shown to eeectively denoise a single noisy copy, we consider in this paper combining the two operations of averaging and thresholding. Because thresholding is a non-linear technique, averaging then ...

This correspondence addresses the recovery of an image from its multiple noisy copies. The standard method is to compute the weighted average of these copies. Since the wavelet thresholding technique has been shown to effectively denoise a single noisy copy, we consider in this paper combining the two operations of averaging and thresholding. Because thresholding is a nonlinear technique, avera...

In this paper, we propose a modified speech enhancement system using wavelet packets thresholding algorithm. First, we describe the basic algorithms of wavelet-based speech enhancement systems and we explain a previous introduced algorithm for improvement of the basic algorithm. Then we discuss on some factors affecting the performance of this system, including the thresholding function and the...

Image segmentation is a basic work in the field of image analysis and computer vision. Thresholding is one of the simplest methods of image segmentation. In general, thresholding approaches based on 1-D histogram do not make use of any space adjacent information of the image, thus it is often ruined by noise; thus, thresholding methods based on 2-D histogram are put forward. These methods have ...

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...

For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving the combinatorial optimization problem associated with compressed sensing. This paper describes an implementation for graphics processing units (GPU) of hard thresholding, iterative hard thresholding, normalized iterative hard thresholding, hard thresholding pursuit, and a two-stage thresholding ...

The method of signal denoising via wavelet thresholding was popularised by Donoho and Johnstone (1994, 1995) and is now widely applied in science and engineering. It is based on thresholding of wavelet coefficients arising from the standard scalar orthogonal discrete wavelet transform (DWT). Recently this approach has been extended to incorporate thresholding coefficients arising from the discr...