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downsampled-noisy-signals

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FS-CodeBase / Deep-Learning-Architectures-for-CompressedNoisy-Image-Signal-Recovery

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Implementation of architectures presented in "Deep Neural Networks for Low-resolution Photon-limited Imaging"

deep-neural-networks mnist-dataset convolutional-neural-networks stacked-denoising-autoencoders poisson-noise mean-squared-error deep-learning-methods compressed-sensing-applications downsampled-noisy-signals optimization-based-algorithms signal-reconstructions poisson-autoencoder-inverting-network
  • Updated Oct 8, 2024
  • Jupyter Notebook

FS-CodeBase / ML-Signal-Classification-and-Reconstruction-Network

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SCRNet is a deep neural network architecture designed to handle compressed and noisy character images signals. Tailored for tasks like character recognition and image signal restoration, SCRNet integrates classification and reconstruction pathways, enhancing performance and robustness through their synergistic interaction

compressed-sensing convolutional-neural-networks emnist-classification poisson-noise emnist-dataset signal-reconstruction emnist-letters downsampled-noisy-signals
  • Updated Sep 26, 2024
  • Jupyter Notebook

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