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
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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.
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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.
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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.
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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.