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Suppose is the area of an image, and and are two points within the image. Then, the algorithm is: [6] = () (,).where () is the filtered value of the image at point , () is the unfiltered value of the image at point , (,) is the weighting function, and the integral is evaluated .
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.
Stable Diffusion (2022-08), released by Stability AI, consists of a denoising latent diffusion model (860 million parameters), a VAE, and a text encoder. The denoising network is a U-Net, with cross-attention blocks to allow for conditional image generation.
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.
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.
"AI slop", often simply "slop", is a term for low-quality media, including writing and images, made using generative artificial intelligence technology. [ 4 ] [ 5 ] [ 1 ] Coined in the 2020s, the term has a derogatory connotation akin to " spam ".
Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
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