A novel framework for object removal from digital photograph
Jhing-Fa Wang, Han‐Jen Hsu, Shang-Chia Liao · 2005
This work aims for a novel function for smart camera-redundant object removal from digital photograph. The proposed novel framework can fill the left lacuna region in the digital image. In previous related researches, texture synthesis and image inpainting construct the fundamentals of filling the lost image region. Texture synthesis can be used to fill the large hole of input texture, while image inpainting can be used to repair the small image gaps. In this paper, we propose an object removal framework by the sub-patch texture synthesis algorithm and weighted interpolation method with automatic repainting mechanism. In the filling process, the color distribution analysis is used to choose different methods. The exhaustive computation time is reduced by the weighted interpolation method. In order to repaint the faulty texture region intelligently, we use the color ratio gradients to detect the synthesized artifact region. The automatic artifact detection can lead repainting the faulty region without user intervention. The proposed algorithm can achieve better performance with seamless output images. The regular computation is also suitable for hardware architecture different from previous existing algorithms.