Digital image processing remains a challenging domain of programming. All digital images contain some degree of noise. Often times this noise is introduced by the camera when a picture is taken. Image denoising algorithms attempt to remove this noise from the image. In this paper the method for image denoising based on the nonlocal means (NL-means) algorithm has been implemented and results hav...

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising a...

Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the r...

This thesis deals with the application of multiple directional transforms to image restoration, and discusses two cases of image restoration, image denoising and image fusion. In Chapter 1, the background of image restoration is described. First, image denoising problems are addressed. Image denoising is a principal problem of image processing and the purpose is to obtain an original picture as...

In this article we argue that when an image is corrupted by additive noise, its curvature image is less affected by it, i.e. the PSNR of the curvature image is larger. We speculate that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experimen...

In all methods of image denoising there is a problem always exists that is how to distinguish noise and edge. Now wavelet and contourlet are main tools in image denoising, but threshold is the key in wavelet and contourlet denoising. In order to distinguish noise and edge well, most methods in wavelet denoising are about the improvement of threshold. Aiming to resolve this problem, a new method...

Denoising and compression is the best crucial technique to overcome this issue. SPHIT is mostly refined type of the algorithm of EZW and it is better algorithms for image compression that makes a set of bit stream from which we can get best renovate images. Through these algorithms, the values of highest PSNR of different type of images compression ration can be acquired. The denoised image per...

The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes...

A novel segmentation scheme for noisy image is proposed. According to the analysis of wavelet denoising method and multiscale geometric analysis techniques, an improved wavelet denoising algorithm combined with multiscale geometric analysis is presented in this paper first. Due to the isotropic nature of wavelet transform, 2D image details are not well represented in wavelet transform, which re...