The iterative thresholding algorithms started in [1] (both soft and hard) and in [2, 3, 4] (soft) for wavelet based linear inverse problems restoration with sparsity constraint. The analysis of iterate soft thresholding algorithms has been well studied under the framework of foward-backward splitting method [5, 6] and inspired many works for different applications and related minimization probl...

The major drawback of watershed transformation is over-segmentation. It also has a significant advantage: very good edge extraction. Thresholding methods usually utilize only global information such as an image histogram; however, they have the ability to group pixels into clusters by their value. The method presented in this paper combines the advantages of watershed segmentation and multileve...

Adaptive binarization methodologies threshold the intensity of pixels with respect to adjacent exploiting integral images. In turn, images are generally computed optimally using summed-area-table algorithm (SAT). This document presents a new adaptive technique based on fuzzy through an efficient design modified SAT for integrals. We define this methodology as FLAT (Fuzzy Local Thresholding). Th...

This work presents an image denoising algorithm, arguably the simplest among all the counterparts, but surprisingly effective. The algorithm exploits the image pixel correlation in the spacial dimension as well as in the color dimension. The color channels of an image are first decorrelated with a 3point orthogonal transform. Each decorrelated channel is then denoised separately via local DCT (...

A digital grayscale image can be described by intensity or pixel values. The gray levels are spread over the images as irregular or inhomogeneous fashion. A number of proposed methods for calculating the optimal thresholding value for image segmentation, but the fractal analysis is an expeditious and significant mathematical approach that distributes with irregular geometric objects. In the rec...

-This paper introduces a new image thresholding method based on minimizing the measures of fuzziness of an input image. The membership function in the thresholding method is used to denote the characteristic relationship between a pixel and its belonging region (the object or the background). In addition, based on the measure of fuzziness, a fuzzy range is defined to find the adequate threshold...

Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The functional usually consists of fidelity term and regularization term. Because functionals may vary from different types images, developing an efficient, simple, general numerical method to minimize them has become increasingly vital. However, many existing met...

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated specific algorithm instead of pre-defined value obtained from observation or experiment. However, the involves per pixel operation, histogram calculation, iterative procedure search optimum that costly for high-resolution images. In this research, parallel implem...