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  2. Video denoising - Wikipedia

    en.wikipedia.org/wiki/Video_denoising

    Video denoising is the process of removing noise from a video signal. Video denoising methods can be divided into: Spatial video denoising methods, where image noise reduction is applied to each frame individually. Temporal video denoising methods, where noise between frames is reduced.

  3. Noise reduction - Wikipedia

    en.wikipedia.org/wiki/Noise_reduction

    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.

  4. Compression artifact - Wikipedia

    en.wikipedia.org/wiki/Compression_artifact

    Video compression artifacts include cumulative results of compression of the comprising still images, for instance ringing or other edge busyness in successive still images appear in sequence as a shimmering blur of dots around edges, called mosquito noise, as they resemble mosquitoes swarming around the object.

  5. Category:Noise reduction - Wikipedia

    en.wikipedia.org/wiki/Category:Noise_reduction

    Image noise reduction techniques (19 P) N. Noise reduction systems (3 C, 9 P) Pages in category "Noise reduction" ... Video denoising This page was last ...

  6. Total variation denoising - Wikipedia

    en.wikipedia.org/wiki/Total_variation_denoising

    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.

  7. Image noise - Wikipedia

    en.wikipedia.org/wiki/Image_noise

    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.

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