Abstract
Image composition is the process of first extracting a candidate region from a candidate image and then embedding this region into a target image. Traditional composite methods focus on reducing appearance gaps (boundary, brightness, color, and sharpness) between the candidate region and the target image. However, in the composite process, low-quality candidate region extraction negatively affects the composite results. In several complicated images, the composite results are not realistic, especially for the boundary. This study proposes an innovative algorithm that can solve the drawback of traditional methods We combined wavelet transform and high-quality candidate region extraction model to achieve composition. On one hand, segmentation and improved matting models are used for high-quality candidate region extraction. On the other hand, wavelet transform is used for multi-scale decomposition, which obviously improves the composite results. Experimental results using a large database show the superiority of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
