Advanced Editing Methods for Image and Video Sequences

The proposed editing methods add new key ingredients, such as camera noise models and global optimization frameworks, that help achieving results that surpass the capabilities of state-of-the-art methods. Using these ingredients, each proposed method defines local visual properties that approximate well the specific editing requirements of each task. These properties are then encoded into a energy function that, when globally minimized, produces the required editing results. The optimization of such energy functions corresponds to Bayesian inference problems that are solved efficiently using graph cuts.

The proposed methods are demonstrated to outperform other state-of-the-art methods. Furthermore, they are demonstrated to work well on complex real-world scenarios that have not been previously addressed in the literature, i.e., highly cluttered scenes for HDR deghosting, and highly dynamic scenes and unconstrained camera motion for object removal from videos.