Image sequence restoration in the presence of pathological motion and severe artifacts

Rares,; Reinders,; Biemond,

doi:10.1109/icassp.2002.1004633

Cited by 4 publications

(2 citation statements)

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“…In the literature, several techniques are used for removing motion artefacts from images, such as periodic moving-average filter methods, FFT methods, SVD methods, wavelet denoising methods and adaptive filtering methods [35]. A spatial restoration algorithm was introduced using a nonlinear interpolation technique for eliminating motion artefacts [36]. This algorithm preserves the edges surrounding the artefact area of the image sequences and is mostly used in biomedical applications.…”

Section: A State-of-the-artmentioning

confidence: 99%

Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration

Rahman

Ahmed

Begum

2020

IEEE Trans. Biomed. Eng.

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In this paper, four physiological parameters, i.e., heart rate (HR), inter-beat-interval (IBI), heart rate variability (HRV), and oxygen saturation (SpO2), are extracted from facial video recordings. Methods: Facial videos were recorded for 10 min each in 30 test subjects while driving a simulator. Four regions of interest (ROIs) are automatically selected in each facial image frame based on 66 facial landmarks. Red-green-blue color signals are extracted from the ROIs and four physiological parameters are extracted from the color signals. For the evaluation, physiological parameters are also recorded simultaneously using a traditional sensor "cStress," which is attached to hands and fingers of test subjects. Results: The Bland Altman plots show 95% agreement between the camera system and "cStress" with the highest correlation coefficient R = 0.96 for both HR and SpO2. The quality index is estimated for IBI considering 100 ms R-peak error; the accumulated percentage achieved is 97.5%. HRV features in both time and frequency domains are compared and the highest correlation coefficient achieved is 0.93. One-way analysis of variance test shows that there are no statistically significant differences between the measurements by camera and reference sensors. Conclusion: These results present high degrees of accuracy of HR, IBI, HRV, and SpO2 extraction from facial image sequences. Significance: The proposed non-contact approach could broaden the dimensionality of physiological parameters extraction using cameras. This proposed method could be applied for driver monitoring application under realistic conditions, i.e., illumination, motion, movement, and vibration.

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Section: A State-of-the-artmentioning

confidence: 99%

Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration

Rahman

Ahmed

Begum

2020

IEEE Trans. Biomed. Eng.

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“…However, when pathological motion occurs (i.e., when the objects in the image are difficult to track), the temporal restoration algorithms fail due to inaccurate motion vectors. Several solutions have been proposed [5,6,7], which discard the temporal information when pathological motion is detected, since this type of information is unreliable. This paper continues the aforementioned restoration efforts and represents a solution that also uses only spatial information during the restoration.…”

Section: Introductionmentioning

confidence: 99%

Constrained Texture Restoration

Rares

Reinders

Biemond

2005

EURASIP J. Adv. Signal Process.

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A method is proposed for filling in missing areas of degraded images through explicit structure reconstruction, followed by texture synthesis. The structure being reconstructed represents meaningful edges from the image, which are traced inside the artefact. The structure reconstruction step relies on different properties of the edges touching the artefact and of the areas between them, in order to sketch the missing edges within the artefact area. The texture synthesis step is based on Markov random fields and is constrained by the traced edges in order to preserve both the shape and the appearance of the various regions in the image. The novelty of our contribution concerns constraining the texture synthesis, which proves to give results superior to the original texture synthesis alone, or to the smoothness-preserving structure-based restoration

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