Image and Signal Denoising Methods

Image and Signal Denoising Methods is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 59k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

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.

• Image Denoising
• Sparse Representations
• Wavelet Transform
• Deep Learning
• Non-Local Means
• Hyperspectral Imaging
• Gaussian Noise
• Anisotropic Diffusion
• Poisson Noise
• Convolutional Neural Networks