Image and Signal Denoising Methods

This cluster of papers encompasses a wide range of techniques and algorithms for image denoising, including sparse representations, wavelet transform, deep learning with convolutional neural networks, non-local means, and methods specific to handling different types of noise such as Gaussian, Poisson, and salt-and-pepper noise. The applications also extend to hyperspectral imaging and the use of anisotropic diffusion for speckle reduction.

How has Image and Signal Denoising Methods's publication output changed over time?

Output grew0% over the shown period — from 1 works in 2007 to 1 in 2020.

What are the most-cited papers on Image and Signal Denoising Methods?

Where is Image and Signal Denoising Methods research published, and who funds it?

How much of the research on Image and Signal Denoising Methods is open access?