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In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.
Significant reduction of this noise can be achieved by applying notch filters in the frequency domain. [7] The following images illustrate an image affected by periodic noise, and the result of reducing the noise using frequency domain filtering. Note that the filtered image still has some noise on the borders.
Noise reduction is the process of removing noise from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some degree. Noise rejection is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with common-mode rejection ratio.
Smoothing the image before downsampling ensures that the resulting image maintains better quality and visual fidelity. [13] Computer Vision: The use of Gaussian filters in computer vision is extensive, including applications in object detection, image segmentation, and feature extraction, where reducing noise is crucial for accurate analysis. [14]
The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.
In digital photography, dark-frame subtraction is a way to reduce image noise in photographs shot with long exposure times, at high ISO sensitivity or at high temperatures. It takes advantage of two components of image noise that remain the same from one shot to the next, dark current and fixed-pattern noise. [1]
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