DeepFSI learns globally optimal measurement masks via end-to-end gradient training under realistic Poisson noise, outperforming PCA-based feature-specific imaging in accuracy and robustness.
A Review Paper: Noise Models in Digital Image Processing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Presents a coupled particle interaction model for images that yields a kinetic density equation, a scaled macroscopic continuum model, and a particle-optimization segmentation method shown to be robust to noise.
Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.
citing papers explorer
-
Deep Feature-specific Imaging
DeepFSI learns globally optimal measurement masks via end-to-end gradient training under realistic Poisson noise, outperforming PCA-based feature-specific imaging in accuracy and robustness.
-
A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models
Presents a coupled particle interaction model for images that yields a kinetic density equation, a scaled macroscopic continuum model, and a particle-optimization segmentation method shown to be robust to noise.
-
Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments
Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-species laser spectroscopy under complex conditions.