Methods › Computer Vision › Image Denoising Models
Image Denoising Models
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 2 of the 5 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 1 of 5 methods:
Generative Adversarial Networks (GANs) are a type of generative model that use two networks, a generator to generate images and a discriminator to discriminate between real and fake, to train a model that approximates the distribution of the data. Below you can find a continuously updating list of GANs.
Text 2, carried by 1 of 5 methods:
Dimensionality Reduction methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.
Methods
All 5 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| PCA Principal Components Analysis | – | 1,323 |
| Noise2Fast | – | 2 |
| DU-GAN | – | 1 |
| JDeskew Adaptive Radial Projection on Fourier Magnitude Spectrum | – | 1 |
| Lower Bound on Transmission using Non-Linear Bounding Function in Single Image Dehazing | – | 1 |