Dictionary learning from incomplete data for efficient image restoration. Naumova, V. & Schnass, K. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1425-1429, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex In real-world image processing applications, the data is high dimensional but the amount of high-quality data needed to train the model is very limited. In this paper, we demonstrate applicability of a recently presented method for dictionary learning from incomplete data, the so-called Iterative Thresholding and K residual Means for Masked data, to deal with high-dimensional data in an efficient way. In particular, the proposed algorithm incorporates a corruption model directly at the dictionary learning stage, also enabling reconstruction of the low-rank component again from corrupted signals. These modifications circumvent some difficulties associated with the efficient dictionary learning procedure in the presence of limited or incomplete data. We choose an image inpainting problem as a guiding example, and further propose a procedure for automatic detection and reconstruction of the low-rank component from incomplete data and adaptive parameter selection for the sparse image reconstruction. We benchmark the efficacy and efficiency of our algorithm in terms of computing time and accuracy on colour, 3D medical, and hyperspectral images by comparing it to its dictionary learning counterparts.
@InProceedings{8081444,
author = {V. Naumova and K. Schnass},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Dictionary learning from incomplete data for efficient image restoration},
year = {2017},
pages = {1425-1429},
abstract = {In real-world image processing applications, the data is high dimensional but the amount of high-quality data needed to train the model is very limited. In this paper, we demonstrate applicability of a recently presented method for dictionary learning from incomplete data, the so-called Iterative Thresholding and K residual Means for Masked data, to deal with high-dimensional data in an efficient way. In particular, the proposed algorithm incorporates a corruption model directly at the dictionary learning stage, also enabling reconstruction of the low-rank component again from corrupted signals. These modifications circumvent some difficulties associated with the efficient dictionary learning procedure in the presence of limited or incomplete data. We choose an image inpainting problem as a guiding example, and further propose a procedure for automatic detection and reconstruction of the low-rank component from incomplete data and adaptive parameter selection for the sparse image reconstruction. We benchmark the efficacy and efficiency of our algorithm in terms of computing time and accuracy on colour, 3D medical, and hyperspectral images by comparing it to its dictionary learning counterparts.},
keywords = {image denoising;image reconstruction;image representation;image restoration;learning (artificial intelligence);real-world image processing applications;high-dimensional data;dictionary learning stage;low-rank component;image inpainting problem;sparse image reconstruction;dictionary learning counterparts;image restoration;masked data;dictionary learning procedure;adaptive parameter selection;Dictionaries;Signal processing algorithms;Machine learning;Manganese;Image color analysis;Image reconstruction;Matching pursuit algorithms},
doi = {10.23919/EUSIPCO.2017.8081444},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346889.pdf},
}Downloads: 0
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