@InProceedings{8903151,
  author = {N. Bonettini and M. Paracchini and P. Bestagini and M. Marcon and S. Tubaro},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Hyperspectral X-ray Denoising: Model-Based and Data-Driven Solutions},
  year = {2019},
  pages = {1-5},
  abstract = {In this paper we deal with the problem of hyperspectral X-Ray image denoising. In particular, we compare a classical model-based Wiener filter solution with a datadriven methodology based on a Convolutional Autoencoder. A challenging aspect is related to the specific kind of 2D signal we are processing: it presents mixed dimensions information since on the vertical axis there is the pixels position while, on the abscissa, there are the different wavelengths associated to the acquired X-Ray spectrum. The goal is to approximate the denoising function using a learning-from-data approach and to verify its capability to emulate the Wiener filter using a much less demanding approach in terms of signal and noise statistical knowledge. We show that, after training, the CNN is able to properly restore the 2D signal with results very close to the Wiener filter, honouring the proper signal shape.},
  keywords = {image denoising;learning (artificial intelligence);statistical analysis;Wiener filters;data-driven solutions;convolutional autoencoder;pixels position;learning-from-data approach;noise statistical knowledge;hyperspectral X-ray image denoising;model-based Wiener filter solution;Convolution;Two dimensional displays;Noise reduction;Hyperspectral imaging;X-ray imaging;Image denoising;Shape;Hyperspectral Imaging;Image Denoising;Convolutional Autoencoder;Machine Vision},
  doi = {10.23919/EUSIPCO.2019.8903151},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533635.pdf},
}

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Tubaro},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Hyperspectral X-ray Denoising: Model-Based and Data-Driven Solutions},\n year = {2019},\n pages = {1-5},\n abstract = {In this paper we deal with the problem of hyperspectral X-Ray image denoising. In particular, we compare a classical model-based Wiener filter solution with a datadriven methodology based on a Convolutional Autoencoder. A challenging aspect is related to the specific kind of 2D signal we are processing: it presents mixed dimensions information since on the vertical axis there is the pixels position while, on the abscissa, there are the different wavelengths associated to the acquired X-Ray spectrum. The goal is to approximate the denoising function using a learning-from-data approach and to verify its capability to emulate the Wiener filter using a much less demanding approach in terms of signal and noise statistical knowledge. 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