LAUSR

Composite ghost is a common phenomenon that widely exists in dynamic scene image mosaic and significantly affects the naturalness of mosaic. To remove the ghost effectively and produce visually natural mosaic, we propose a novel image mosaic method by jointly identifying composite ghost and eliminating ghost regions without distorting, splitting, and duplicating objects. Specifically, our main contributions are three-fold: First, we propose the motion-aware composite ghost identification to localize the potential composite ghosts in the mosaic region (i.e., overlapping area between two images to be stitched) by detecting the salient-moving objects in two stitched images. Second, we design the object-aware alternative region selection strategy to produce ghostless regions that can replace the localized composite ghosts while avoiding object distortion, object separation, and object repetition. Third, we realize the image interpolation-based composite ghost elimination that can generate natural stitched image by eliminating the composite ghost of the initial blending result with the selected image source. We validate the proposed method on challenging datasets and show that our method outperform the state-of-the-art methods.