Gaussian white noise detectionDeep learningSince the theory of generative adversarial nets (GANs) put forward in 2014, various applications based on GANs have been developed. Most of the applications focused on generator network (G) of GANs to solve the daily challenges. However, rare of them ...
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Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing t...
Gaussian blur is a technique used in image processing, often in Photoshop, to smooth out the noise and grainy appearance in an image. By reducing the difference in pixel values, it helps create a soft, natural-looking blur. Gaussian blur is particularly useful in low-light photos or when ...
Gaussian blur is an essential part of manyimage processing algorithms. It serves to clear the noise in the images, as well as a general visual effect in various graphics software. The followingphoto utilityuses a Gaussian filter to blur images right in a web browser. ...
Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and ...
Experimental results on both synthetic and real datasets demonstrate superior performance, achieving a peak signal-to-noise ratio of 26.11 dB and structural similarity of 0.89 on synthetic images, while preserving more background details and effectively supporting downstream tasks like object segmentation....
In Chapter 2, we develop a simple signal processing technique that can reduce additive Gaussian noise in some situations. Since noise is random, a time function or time plot is not particularly useful. It is more common to discuss other properties of noise such as its probability distribution,...
I have a Gaussian noise corrupted image and I need to know how can I find the MMSE estimate of the entire noisy image in MATLAB? or how can I find the MMSE estimate of a Gaussian noise vector/signal (because I can divide the entire image into small patch vectors)?
White noise and colored noise are important signals in stochastic systems. White noise: In signal processing, white noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. White noise draws its name from white light, although light ...