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The key idea is to learn a patient-specific model of the relationships between signals, and then reconstruct corrupted segments using the information available in correlated signals.
The presence of overlapping HSPs (removed by RemoveHit) does not sufficiently indicate corrupted segments.
The technique sends several data streams of information simultaneously and combines the least corrupted segments of each in an optimal way to provide signal redundancy.
The fundamental idea is to learn a set of morphological templates, and reconstruct the corrupted segments using them.
Applications include, e.g., creating audio textures, music remixing, and replacing corrupted segments in an encoded audio file with synthesized ones.
Our technique is based upon learning a patient-specific model of the relationships between time-aligned signals and then reconstructing corrupted segments using the information available in correlated signals.
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Fig. 7 Example of corrupted image segment for case 4 of case A. Find the number of non-noisy pixels (k) in the current processing window.
Fig. 3 Example of corrupted image segment for case 2 of case A. If k is equal to 2, then increase the window size by 2 to get the window size 5 × 5 and then find the weights from their positions of non-noisy pixels.
Fig. 5 Example of corrupted image segment for case 3 of case A. If k is equal to 1, then increase the window size by 4 to get the window size 7 × 7 and then find the weights from their positions of the non-noisy pixels.
Fig. 9 Example of corrupted image segment for case B. The inverse distance weighted interpolation filter is evaluated based on different metrics such as peak signal-to-noise ratio (PSNR), image enhancement factor (IEF), and error rate which is given in Eqs. 3, 5, and 6, respectively.
For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest.
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