Parallel scale de-blur net for sharpening video images for remote clinical assessment of hand movements

Clinicians and researchers commonly assess hand movements to detect and monitor neurological disorders. With the growing use of deep learning and biomedical informatics, computer vision can be applied to hand movement videos to extract movement features. Such methods promise objective and automated measures of hand movements which can potentially reveal richer details than clinicians in a face-to-face setting. However, extracting valid measures from hand movement video data is a challenging task because motion blur occurs when the hands move quickly. To address this issue, current de-blurring methods have been investigated and a novel 'Parallel Scale Deblur Net' (PSDNet) is proposed for hand movement image de-blurring. The results demonstrate that PSDNet achieves better de-blurring performance on both a general blur dataset (available online) and also on our own hand motion dataset.