Video completion in the presence of moving subjects based on segmentation using Neutrosophic sets

Image and video completion are essential tasks in the field of image and video processing, often used for restoring damaged regions in images and video frames. The primary challenge in these tasks is to complete them in such a way that they do not introduce noticeable artifacts or inconsistencies to the viewer. While image completion focuses on filling in missing parts in a static context, video completion requires additional considerations due to the temporal dimension. The motion of objects and the preservation of temporal consistency are critical factors in video completion. This research proposes a novel method for image and video completion based on Neutrosophic theory, which handles uncertainty in both spatial and intensity domains. Neutrosophy is utilized to interpret the indeterminacy present in images, allowing for more accurate segmentation and better handling of incomplete data. The proposed method first segments the image using Neutrosophic-based segmentation and then uses the segmented information to guide the completion of missing regions. For video completion, a two-step approach is introduced that separates static backgrounds from moving objects. The background is reconstructed using image completion based on Neutrosophic-based segmentation, and the foreground is completed by identifying appropriate data that best match the missing parts; this data is chosen using a contour-based method, which this method applies neutrosophic sets to get to the most suitable data. The novelty of the approach lies in several key contributions: 1) the use of Neutrosophic theory to handle spatial and intensity uncertainties, 2) a Neutrosophic-based similarity measure for image segmentation, 3) a new metric for finding the most suitable patch for hole filling, and 4) a novel method for preserving boundaries and uniformity in video completion, particularly in the presence of moving objects. Experimental results demonstrate the effectiveness of the proposed methods, with improved visual quality and reduced inconsistencies compared to previous state-of-the-art methods. However, challenges remain in applying the method to highly detailed images with many classes and handling dynamic backgrounds.