Denoising of images corrupted by Gaussian noise using wavelet transform is of great concern in the past two decades. In wavelet denoising method, detail wavelet coefficients of noisy image are thresholded using a specific thresholding function by comparing to a specific threshold value, and then applying inverse wavelet transform, results in denoised image. Recently, an effective image denoisin...

In [5], a general framework for adaptive function regularization was introduced, and this framework was demonstrated in several applications, including image denoising. The basic idea of the method applied to image denoising is to choose a set of features and consider the pixels of the image as lying in feature space. We then try to use the heat equation on the points in feature space to smooth...

The noise degrades performance of image processing algorithms in brain imaging. Image denoising methods are important image processing algorithms which are used to reduce the noise. Brain image denoising is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate process of brain images is a very ...

Image Denoising is a major research topic for many image processing researchers. In some image Denoising algorithms, the user is required to specify the number of smooth image regions to estimate the noise level. In order to overcome this difficulty, we adopt a segmentation-based approach to estimate the noise level from a single image. The image is partitioned into smooth regions, in which the...

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean image patches. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount o...

In image processing, the most significant challenges have been addressed due to the image denoising. Since, the segmentation of the original image components from the noisy image has high complexity. Therefore, the different image segmentation techniques were developed for effective segmentation by categorizing the noisy images. Among different methods, the image denoising including with the ed...

Preprocessing, a major component of Character Recognition System, has direct effect on the recognition system by its performance. Using wavelet transform, this paper mainly focuses on noise filtering and Edge Detection in a Preprocessing method for NaXi Pictographs Character Recognition. Image denoising is an important issue in image preprocessing. Based on the analysis of thresholding function...