Image Matting using Neural Networks

Abstract

Image matting, also refers to picture matting in the article, is the task of finding appealing targets in a picture or sequence of pictures i.e., video, and it has been used extensively in many photo and video editing applications. Image composition is the process of extracting an eye-catching subject from a photograph and blending it with a different background. a) Blue/Green screen (curtain) matting, where the backdrop is clear and readily distinct between the foreground (frontal area) and background (foundation) portions. This approach is now the most used type of image matting. b) Natural picture matting, in which these sorts of photos are taken naturally using cameras or cell phones during everyday activities. These are the present known techniques of picture matting. It is difficult to discern the distinction between the frontal area and the foundation at their boundaries. The current framework requires both the RGB and trimap images as inputs for natural picture matting. It is difficult to compute the trimap since additional framework is required to obtain this trimap. This study will introduce the Picture Matting Neural Net (PMNN) framework, which utilizes a single RGB image as an input and creates the alpha matte without any human involvement in between the framework and the user, to overcome the drawbacks of the prior frameworks. The created alpha matte is tested against the alpha matte from the PPM-100 data set, and the PSNR and SSIM measurement index are utilized to compare the two. The framework works well and can be fed with regular pictures taken with cameras or mobile phones without reducing the clarity of the image.