We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a componentwise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally des...

Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement...

Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction...

Many images like medical images, satellite images, Arial images and even life photographs suffer from noise. Image enhancement techniques are used to improve the visual appearance of images by reducing noise from images. In this paper, a dynamic stochastic resonance (DSR) based algorithm is proposed to improve the visual quality by removing noise from digital image. Stochastic resonance (SR) is...

Radiation dose has raised significant concerns to patients and operators in modern X-ray computed tomography (CT) examinations. A simple and cost-effective means to perform a low-dose CT scan is to lower the milliampere-seconds (mAs) as low as reasonably achievable in data acquisition. However, the associated image quality with lower-mAs scans (or low-dose scans) will be unavoidably degraded du...

This paper presents an algorithm for noise removal from digital image, based on stochastic noise pattern. We apply white Gaussian noise to improve the quality of the noisy input image to get the de -noised response image. Input noisy image is subjected to independent additive white Gaussian noise of different standard deviation, the output image corresponding to individual noise standard deviat...

The goal of this paper is to propose a generalized signal-dependent noise model that is more appropriate to describe a natural image acquired by a digital camera than the conventional Additive White Gaussian Noise model widely used in image processing. This non-linear noise model takes into account effects in the image acquisition pipeline of a digital camera. In this paper, an algorithm for es...

The de-noising of an image corrupted by Gaussian noise is a classical problem in signal or image processing. An image is often corrupted by noise during its acquisition and transmission. Image de-noising is used to reduce the noise while retaining the important features in the image. Always there exists a tradeoff between the removed noise and the blurring in the image. The use of wavelet trans...

conclusions in the case of existence gaussian noise, the results confirm that denoising is not effective on the measurement of t2* value. in the case of image distortion by rician noise, a predictor model is proposed to estimate the original t2* value. the predictor model is used to estimate the t2* value of new patients. the predicted t2* values were in good agreement with the corresponding or...

Problem statement: One of the most common degradations in medical images is their poor contrast quality and noise. The DICOM image consists of speckle (multiplicative noise). While the image is enhanced, the multiplicative noise present in the image is also enhanced. Approach: This study describes the hybrid method to improve the image quality of Digital Imaging and Communications in Medicine (...