We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering both an isotropic and an anisotropic ℓ 0 regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied in [1] for signal recovery problems. For the numerical realisation of the model, we propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers. We test our model on highly-degraded synthetic and real-world data and quantitatively compare our results with several sparsity-promoting variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that thanks to the ℓ 0 smoothing on the gradient, the super-resolved images can be used to improve the accuracy of standard segmentation algorithms for applications like QR codes and cell detection and land-cover classification problems.

Efficient ℓ Gradient-Based Super-Resolution for Simplified Image Segmentation / Cascarano P.; Calatroni L.; Loli Piccolomini E.. - In: IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING. - ISSN 2333-9403. - ELETTRONICO. - 7:(2021), pp. 9394806.399-9394806.408. [10.1109/TCI.2021.3070720]

Efficient ℓ Gradient-Based Super-Resolution for Simplified Image Segmentation

Cascarano P.
;
Loli Piccolomini E.
2021

Abstract

We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering both an isotropic and an anisotropic ℓ 0 regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied in [1] for signal recovery problems. For the numerical realisation of the model, we propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers. We test our model on highly-degraded synthetic and real-world data and quantitatively compare our results with several sparsity-promoting variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that thanks to the ℓ 0 smoothing on the gradient, the super-resolved images can be used to improve the accuracy of standard segmentation algorithms for applications like QR codes and cell detection and land-cover classification problems.
2021
Efficient ℓ Gradient-Based Super-Resolution for Simplified Image Segmentation / Cascarano P.; Calatroni L.; Loli Piccolomini E.. - In: IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING. - ISSN 2333-9403. - ELETTRONICO. - 7:(2021), pp. 9394806.399-9394806.408. [10.1109/TCI.2021.3070720]
Cascarano P.; Calatroni L.; Loli Piccolomini E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/847238
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