An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images

This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders an...

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Veröffentlicht in:Journal of mathematical imaging and vision 2024-10, Vol.66 (5), p.904-925
Hauptverfasser: Boccuto, Antonio, Gerace, Ivan, Giorgetti, Valentina, Martinelli, Francesca, Tonazzini, Anna
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container_issue 5
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container_title Journal of mathematical imaging and vision
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creator Boccuto, Antonio
Gerace, Ivan
Giorgetti, Valentina
Martinelli, Francesca
Tonazzini, Anna
description This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.
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subjects Algorithms
Applications of Mathematics
Color imagery
Computer Science
Datasets
Derivatives
Image filters
Image Processing and Computer Vision
Mathematical Methods in Physics
Regularization
Signal,Image and Speech Processing
Smoothness
title An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images
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