1+N fusion: Cascaded self-portrait enhancement
Shuai Yang, Jiaying Liu, Sifeng Xia, Zongming Guo
Abstract
In this paper, we present a novel cascaded framework to solve a self-portrait enhancement problem we call “1+N” problem, in which a self-portrait is enhanced with the help of N supporting photos that share the same scene and similar shooting time. The key idea is to exploit the extra information of these N photos to expand the field of view of the self-portrait and improve its lighting style. We achieve this by alternatingly optimizing two complementary tasks, namely illumination unification and photo registration. Based on the correspondences extracted in the input 1+N photos, our method estimates and updates the illumination and registration coefficients in a cascaded manner. Then a Markov Random Field formulation is proposed to globally fuse the aligned photos. Experimental results demonstrate the proposed method achieves high-quality results in this novel application scenario.
BibTeX
@inproceedings{icassp2017_1nfusioncascaded,
title = {1+N fusion: Cascaded self-portrait enhancement},
author = {Shuai Yang and Jiaying Liu and Sifeng Xia and Zongming Guo},
booktitle = {ICASSP 2017},
year = {2017}
}