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Regularized non-local Total Variation and application in image restoration

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Academic year: 2021

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Figure

Fig. 1 A synthetic image with thin lines contaminated by 11 × 11 missing squares. All methods fail to follow the Connectivity Principle properly, but RNLTV can connect the broken thin lines.
Table 1 The different data-fidelity terms used in the ex- ex-periments.
Fig. 4 Results of different methods when restoring the degraded synthetic image with the checkerboard and word masks
Table 2 Values of the parameters γ of RNLTV for the restoration of the images in Figure 9, when corrupted by the checkerboard or word mask (see Figure 8)
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